https://arxiv.org/api/dI8RHT/19LepDBPvLvAmtgGVz8E 2026-09-16T19:33:26Z 30 50 0 http://arxiv.org/abs/2608.31033v1 2026-08-31T16:14:21Z

Lightstage facial capture produces production-quality digital humans, but it is resource and labor-intensive. Multi-camera setups, hours of computation, and massive data storage create bottlenecks that hinder iterative workflows. This paper introduces FaceSnap, an end-to-end framework that streamlines capture via a two-stage approach. First, a one-time multi-view optimization from a range-of-motion sequence builds a personalized model encoding both geometry and expression-dependent appearance. This model then enables high-fidelity real-time facial performance capture from a single monocular lightstage camera, with no further multi-view capture required. FaceSnap jointly estimates geometry and dynamic 4K texture at 83 fps. The 4K texture is produced by a novel personalized residual upscaler that recovers subject-specific high-frequency detail, which generic upscalers fail to capture. FaceSnap achieves geometric accuracy competitive with full per-frame multi-view optimization while outperforming feed-forward methods trained on production-quality 3D data, all from a single camera view. Finally, we introduce Multi4D, a public benchmark for evaluating 4D facial reconstruction methods in lightstage environments, enabling topology-invariant geometric comparison across methods.

2026-08-31T16:14:21Z Rukhshanda Hussain Noé Artru Emeline Got Luiz Gustavo Hafemann Alexandre Messier Brandon Dearlove Rafael M. O. Cruz Abdallah Dib Eric Granger http://arxiv.org/abs/2607.23687v3 2026-09-11T09:15:01Z

Parametric models of the human head are essential tools traditionally used in computer vision and graphics for animation, rendering, and reconstruction. More recently, they serve as crucial conditioning signals within generative large vision models, allowing for tight spatial control of generated imagery. However, existing publicly available models are typically limited in anatomical scope, modeling only outer geometry while ignoring intra-oral and ocular structures, and frequently suffer from reduced geometric quality stemming from low-fidelity input datasets. In this report we introduce a new parametric model dubbed Generative aNthropometric Model (GNM), named as a homophone of the human genome. GNM encompasses the head, face, neck, eyeballs, teeth, and tongue, and it is built on an extensive database of high-resolution 3D scans combined with high-quality anatomy specific artist-made samples. This report details the data provenance, the model architecture including the specialized sub-models for the ocular and intra-oral structures, and shows its SotA performance on fitting target 3D face scans. To foster community innovation, the complete GNM framework is made publicly available.

2026-07-26T14:34:19Z The GNM is publicly available at: https://github.com/google/GNM Stylianos Ploumpis Jan Bednarik Gaspard Zoss Ruslan Guseinov Luca Prasso Prashanth Chandran Oliver Boyne Vasileios Choutas Timo Bolkart Daoye Wang Menglei Chai Di Qiu Sebastian Winberg Gilles Rainer Lewis Bridgeman Leonhard Helminger Edo Collins Delio Vicini Jérémy Riviere Yannick Boetzel Alexander Koumis Stylianos Moschoglou Jay Busch Cynthia Herrera Jacob Still Scott Ysebert Peter Lincoln Sergio Orts Escolano Christoph Rhemann Erroll Wood Thabo Beeler Stefanos Zafeiriou http://arxiv.org/abs/2605.09089v2 2026-08-10T21:39:20Z

Digital onboarding and eKYC systems used by banks, fintech platforms, telecom providers, and other third-party services commonly verify users by comparing an uploaded identity document with a selfie or live facial capture. This workflow is convenient, but it also makes verification systems vulnerable to localised document manipulations, such as replacing the facial photograph, editing textual identity fields, or altering both. Existing image-forgery detectors are largely designed for natural images and do not explicitly account for the structured layout of identity documents, where security-relevant information is concentrated in specific semantic fields. We propose FLiD, a lightweight field-localised framework for identity-document forgery detection. Instead of processing the full document image, FLiD localises the facial and textual regions using a fine-tuned YOLO11 detector, extracts compact representations with a frozen MobileNetV3-Small backbone, and classifies field-level forgeries using a small 191K-parameter head. Face and text detectors are trained independently and combined through score-level fusion for documents containing simultaneous manipulations. Under 5-fold cross-validation, FLiD achieves AUC scores of 0.834, 0.926, and 0.837 for face, text, and combined attacks, respectively, while reducing Equal Error Rate by 28--29 percentage points compared with a full-document baseline. FLiD also outperforms general-purpose forgery detectors while requiring 13x fewer trainable parameters and 21x fewer FLOPs per field (~8x per full multi-field document), making it a promising candidate for data and resource constrained KYC deployment.

2026-05-09T17:53:54Z Abhishek Kumar Riya Tapwal Carsten Maple Mark Hooper http://arxiv.org/abs/2605.05636v1 2026-05-07T03:43:24Z

High-quality facial appearance capture has traditionally required costly studio recording. Recent works consider an in-the-wild smartphone-based setup; however, their model-based inverse rendering paradigm struggles with the complex disentanglement of reflectance from unknown illumination. To bridge this gap, we propose to shift the paradigm into training a powerful delighting network as a prior to constrain the optimization. We leverage the OLAT dataset and the rendered Light Stage scans for training, and propose Dataset Latent Modulation (DLM) to seamlessly integrate these heterogeneous data sources. Specifically, by conditioning the core network on learnable source-aware tokens, we decouple dataset-specific styles from physical delighting principles, enabling the emergence of a delighting prior that outperforms existing proprietary models. This powerful delighting prior enables a simple and automatic appearance capture pipeline that achieves high-quality reflectance estimation from casual video inputs, outperforming prior arts by a large margin. Furthermore, we leverage our appearance capture method to transform the multi-view NeRSemble dataset into NeRSemble-Scan, a large-scale collection of 4K-resolution relightable scans. By open-sourcing our model and the NeRSemble-Scan dataset, we democratize high-end facial capture and provide a new foundation for the research community to build photorealistic digital humans.

2026-05-07T03:43:24Z ACM Transactions on Graphics (Proc. of SIGGRAPH), 2026. Code: https://github.com/yxuhan/OpenDelight Project Page: https://yxuhan.github.io/OpenDelight/index.html Yuxuan Han Xin Ming Tianxiao Li Zhuofan Shen Qixuan Zhang Lan Xu Feng Xu http://arxiv.org/abs/2509.24577v1 2025-09-29T10:34:13Z

Building a joint face-skull morphable model holds great potential for applications such as remote diagnostics, surgical planning, medical education, and physically based facial simulation. However, realizing this vision is constrained by the scarcity of paired face-skull data, insufficient registration accuracy, and limited exploration of reconstruction and clinical applications. Moreover, individuals with craniofacial deformities are often overlooked, resulting in underrepresentation and limited inclusivity. To address these challenges, we first construct a dataset comprising over 200 samples, including both normal cases and rare craniofacial conditions. Each case contains a CT-based skull, a CT-based face, and a high-fidelity textured face scan. Secondly, we propose a novel dense ray matching registration method that ensures topological consistency across face, skull, and their tissue correspondences. Based on this, we introduce the 3D Bidirectional Face-Skull Morphable Model (BFSM), which enables shape inference between the face and skull through a shared coefficient space, while also modeling tissue thickness variation to support one-to-many facial reconstructions from the same skull, reflecting individual changes such as fat over time. Finally, we demonstrate the potential of BFSM in medical applications, including 3D face-skull reconstruction from a single image and surgical planning prediction. Extensive experiments confirm the robustness and accuracy of our method. BFSM is available at https://github.com/wang-zidu/BFSM

2025-09-29T10:34:13Z Under review Zidu Wang Meng Xu Miao Xu Hengyuan Ma Jiankuo Zhao Xutao Li Xiangyu Zhu Zhen Lei http://arxiv.org/abs/2505.22344v2 2025-11-17T02:14:26Z

Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary constraints. To achieve this, we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling. We then synthesize new LiDAR systems by modeling sensors as parametric distributions in 6D space and fitting these distributions to our learned implicit density using expectation-maximization, enabling efficient, constraint-aware LiDAR system design. We validate our method on diverse tasks in 3D vision, enabling automated LiDAR system design across real-world-inspired applications in face scanning, robotic tracking, and object detection.

2025-05-28T13:27:42Z Nikhil Behari Aaron Young Tzofi Klinghoffer Akshat Dave Ramesh Raskar http://arxiv.org/abs/2505.00380v1 2025-05-01T08:15:48Z

Cross-spectral face recognition systems are designed to enhance the performance of facial recognition systems by enabling cross-modal matching under challenging operational conditions. A particularly relevant application is the matching of near-infrared (NIR) images to visible-spectrum (VIS) images, enabling the verification of individuals by comparing NIR facial captures acquired with VIS reference images. The use of NIR imaging offers several advantages, including greater robustness to illumination variations, better visibility through glasses and glare, and greater resistance to presentation attacks. Despite these claimed benefits, the robustness of NIR-based systems against presentation attacks has not been systematically studied in the literature. In this work, we conduct a comprehensive evaluation into the vulnerability of NIR-VIS cross-spectral face recognition systems to presentation attacks. Our empirical findings indicate that, although these systems exhibit a certain degree of reliability, they remain vulnerable to specific attacks, emphasizing the need for further research in this area.

2025-05-01T08:15:48Z 10 pages Anjith George Sebastien Marcel http://arxiv.org/abs/2504.19718v3 2025-05-24T10:20:30Z

Face registration deforms a template mesh to closely fit a 3D face scan, the quality of which commonly degrades in non-skin regions (e.g., hair, beard, accessories), because the optimized template-to-scan distance pulls the template mesh towards the noisy scan surface. Improving registration quality requires a clean separation of skin and non-skin regions on the scan mesh. Existing image-based (2D) or scan-based (3D) segmentation methods however perform poorly. Image-based segmentation outputs multi-view inconsistent masks, and they cannot account for scan inaccuracies or scan-image misalignment, while scan-based methods suffer from lower spatial resolution compared to images. In this work, we introduce a novel method that accurately separates skin from non-skin geometry on 3D human head scans. For this, our method extracts features from multi-view images using a frozen image foundation model and aggregates these features in 3D. These lifted 2D features are then fused with 3D geometric features extracted from the scan mesh, to then predict a segmentation mask directly on the scan mesh. We show that our segmentations improve the registration accuracy over pure 2D or 3D segmentation methods by 8.89% and 14.3%, respectively. Although trained only on synthetic data, our model generalizes well to real data.

2025-04-28T12:13:12Z 4 pages, 4 figures, published in Eurographics 2025 as a short paper Victoria Yue Chen Daoye Wang Stephan Garbin Jan Bednarik Sebastian Winberg Timo Bolkart Thabo Beeler http://arxiv.org/abs/2504.15259v2 2026-02-04T20:55:54Z

Digital modeling and reconstruction of human faces serve various applications. However, its availability is often hindered by the requirements of data capturing devices, manual labor, and suitable actors. This situation restricts the diversity, expressiveness, and control over the resulting models. This work aims to demonstrate that a semantically controllable generative network can provide enhanced control over the digital face modeling process. To enhance diversity beyond the limited human faces scanned in a controlled setting, we introduce a novel data generation pipeline that creates a high-quality 3D face database using a pre-trained diffusion model. Our proposed normalization module converts synthesized data from the diffusion model into high-quality scanned data. Using the 44,000 face models we obtained, we further developed an efficient GAN-based generator. This generator accepts semantic attributes as input, and generates geometry and albedo. It also allows continuous post-editing of attributes in the latent space. Our asset refinement component subsequently creates physically-based facial assets. We introduce a comprehensive system designed for creating and editing high-quality face assets. Our proposed model has undergone extensive experiment, comparison and evaluation. We also integrate everything into a web-based interactive tool. We aim to make this tool publicly available with the release of the paper.

2025-04-21T17:38:50Z Accepted Manuscript Yunxuan Cai Sitao Xiang Zongjian Li Haiwei Chen Yajie Zhao 10.1145/3793859 http://arxiv.org/abs/2501.19140v3 2025-04-18T09:47:07Z

Multimodal image registration plays a key role in creating digital patient models by combining data from different imaging techniques into a single coordinate system. This process often involves multiple sequential and interconnected transformations, which must be well-documented to ensure transparency and reproducibility. In this paper, we propose the use of transformation trees as a method for structured recording and management of these transformations. This approach has been implemented in the dpVision software and uses a dedicated .dpw file format to store hierarchical relationships between images, transformations, and motion data. Transformation trees allow precise tracking of all image processing steps, reduce the need to store multiple copies of the same data, and enable the indirect registration of images that do not share common reference points. This improves the reproducibility of the analyses and facilitates later processing and integration of images from different sources. The practical application of this method is demonstrated with examples from orthodontics, including the integration of 3D face scans, intraoral scans, and CBCT images, as well as the documentation of mandibular motion. Beyond orthodontics, this method can be applied in other fields that require systematic management of image registration processes, such as maxillofacial surgery, oncology, and biomechanical analysis. Maintaining long-term data consistency is essential for both scientific research and clinical practice. It enables easier comparison of results in longitudinal studies, improves retrospective analysis, and supports the development of artificial intelligence algorithms by providing standardized and well-documented datasets. The proposed approach enhances data organization, allows for efficient analysis, and facilitates the reuse of information in future studies and diagnostic procedures.

2025-01-31T13:49:16Z 28 pages, 15 figures Computers in Biology and Medicine, Vol 193, year: 2025, pages: 110311 Agnieszka Anna Tomaka Dariusz Pojda Michał Tarnawski Leszek Luchowski 10.1016/j.compbiomed.2025.110311 http://arxiv.org/abs/2410.09862v1 2024-10-13T14:47:31Z

High anisotropy in volumetric medical images can lead to the inconsistent quantification of anatomical and pathological structures. Particularly in optical coherence tomography (OCT), slice spacing can substantially vary across and within datasets, studies, and clinical practices. We propose to standardize OCT volumes to less anisotropic volumes by conditioning 3D diffusion models with en face scanning laser ophthalmoscopy (SLO) imaging data, a 2D modality already commonly available in clinical practice. We trained and evaluated on data from the multicenter and multimodal MACUSTAR study. While upsampling the number of slices by a factor of 8, our method outperforms tricubic interpolation and diffusion models without en face conditioning in terms of perceptual similarity metrics. Qualitative results demonstrate improved coherence and structural similarity. Our approach allows for better informed generative decisions, potentially reducing hallucinations. We hope this work will provide the next step towards standardized high-quality volumetric imaging, enabling more consistent quantifications.

2024-10-13T14:47:31Z Accepted at NeurIPS 2024 Workshop on GenAI for Health Coen de Vente on behalf of the MACUSTAR consortium Mohammad Mohaiminul Islam on behalf of the MACUSTAR consortium Philippe Valmaggia on behalf of the MACUSTAR consortium Carel Hoyng on behalf of the MACUSTAR consortium Adnan Tufail on behalf of the MACUSTAR consortium Clara I. Sánchez on behalf of the MACUSTAR consortium http://arxiv.org/abs/2409.16382v1 2024-09-24T18:33:57Z

Recognizing pain in video is crucial for improving patient-computer interaction systems, yet traditional data collection in this domain raises significant ethical and logistical challenges. This study introduces a novel approach that leverages synthetic data to enhance video-based pain recognition models, providing an ethical and scalable alternative. We present a pipeline that synthesizes realistic 3D facial models by capturing nuanced facial movements from a small participant pool, and mapping these onto diverse synthetic avatars. This process generates 8,600 synthetic faces, accurately reflecting genuine pain expressions from varied angles and perspectives. Utilizing advanced facial capture techniques, and leveraging public datasets like CelebV-HQ and FFHQ-UV for demographic diversity, our new synthetic dataset significantly enhances model training while ensuring privacy by anonymizing identities through facial replacements. Experimental results demonstrate that models trained on combinations of synthetic data paired with a small amount of real participants achieve superior performance in pain recognition, effectively bridging the gap between synthetic simulations and real-world applications. Our approach addresses data scarcity and ethical concerns, offering a new solution for pain detection and opening new avenues for research in privacy-preserving dataset generation. All resources are publicly available to encourage further innovation in this field.

2024-09-24T18:33:57Z Pain Recognition Synthetic Data Video Analysis Privacy Preserving Jonas Nasimzada Jens Kleesiek Ken Herrmann Alina Roitberg Constantin Seibold http://arxiv.org/abs/2408.01218v1 2024-08-02T12:16:07Z

3D textured face reconstruction from sketches applicable in many scenarios such as animation, 3D avatars, artistic design, missing people search, etc., is a highly promising but underdeveloped research topic. On the one hand, the stylistic diversity of sketches leads to existing sketch-to-3D-face methods only being able to handle pose-limited and realistically shaded sketches. On the other hand, texture plays a vital role in representing facial appearance, yet sketches lack this information, necessitating additional texture control in the reconstruction process. This paper proposes a novel method for reconstructing controllable textured and detailed 3D faces from sketches, named S2TD-Face. S2TD-Face introduces a two-stage geometry reconstruction framework that directly reconstructs detailed geometry from the input sketch. To keep geometry consistent with the delicate strokes of the sketch, we propose a novel sketch-to-geometry loss that ensures the reconstruction accurately fits the input features like dimples and wrinkles. Our training strategies do not rely on hard-to-obtain 3D face scanning data or labor-intensive hand-drawn sketches. Furthermore, S2TD-Face introduces a texture control module utilizing text prompts to select the most suitable textures from a library and seamlessly integrate them into the geometry, resulting in a 3D detailed face with controllable texture. S2TD-Face surpasses existing state-of-the-art methods in extensive quantitative and qualitative experiments. Our project is available at https://github.com/wang-zidu/S2TD-Face .

2024-08-02T12:16:07Z ACM MM 2024 Zidu Wang Xiangyu Zhu Jiang Yu Tianshuo Zhang Zhen Lei http://arxiv.org/abs/2407.10193v1 2024-07-14T13:24:17Z

Deep learning-based multi-view facial capture methods have shown impressive accuracy while being several orders of magnitude faster than a traditional mesh registration pipeline. However, the existing systems (e.g. TEMPEH) are strictly restricted to inference on the data captured by the same camera array used to capture their training data. In this study, we aim to improve the generalization ability so that a trained model can be readily used for inference (i.e. capture new data) on a different camera array. To this end, we propose a more generalizable initialization module to extract the camera array-agnostic 3D feature, including a visual hull-based head localization and a visibility-aware 3D feature aggregation module enabled by the visual hull. In addition, we propose an ``update-by-disagreement'' learning strategy to better handle data noise (e.g. inaccurate registration, scan noise) by discarding potentially inaccurate supervision signals during training. The resultant generalizable and robust topologically consistent multi-view facial capture system (GRAPE) can be readily used to capture data on a different camera array, reducing great effort on data collection and processing. Experiments on the FaMoS and FaceScape datasets demonstrate the effectiveness of the proposed method.

2024-07-14T13:24:17Z Jing Li Di Kang Zhenyu He http://arxiv.org/abs/2402.19477v2 2024-09-03T14:24:49Z

Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits. Today's methods are data-driven, where the actuations for finite elements are inferred from captured skin geometry. Unfortunately, these approaches have not been widely adopted due to the complexity of initializing the material space and learning the deformation model for each character separately, which often requires a skilled artist followed by lengthy network training. In this work, we aim to make physics-based facial animation more accessible by proposing a generalized physical face model that we learn from a large 3D face dataset. Once trained, our model can be quickly fit to any unseen identity and produce a ready-to-animate physical face model automatically. Fitting is as easy as providing a single 3D face scan, or even a single face image. After fitting, we offer intuitive animation controls, as well as the ability to retarget animations across characters. All the while, the resulting animations allow for physical effects like collision avoidance, gravity, paralysis, bone reshaping and more.

2024-02-29T18:59:31Z Lingchen Yang Gaspard Zoss Prashanth Chandran Markus Gross Barbara Solenthaler Eftychios Sifakis Derek Bradley 10.1145/3658189 http://arxiv.org/abs/2312.05924v2 2023-12-12T10:21:12Z

The popularity of Machine Learning as a Service (MLaaS) has led to increased concerns about Model Stealing Attacks (MSA), which aim to craft a clone model by querying MLaaS. Currently, most research on MSA assumes that MLaaS can provide soft labels and that the attacker has a proxy dataset with a similar distribution. However, this fails to encapsulate the more practical scenario where only hard labels are returned by MLaaS and the data distribution remains elusive. Furthermore, most existing work focuses solely on stealing the model accuracy, neglecting the model robustness, while robustness is essential in security-sensitive scenarios, e.g., face-scan payment. Notably, improving model robustness often necessitates the use of expensive techniques such as adversarial training, thereby further making stealing robustness a more lucrative prospect. In response to these identified gaps, we introduce a novel Data-Free Hard-Label Robustness Stealing (DFHL-RS) attack in this paper, which enables the stealing of both model accuracy and robustness by simply querying hard labels of the target model without the help of any natural data. Comprehensive experiments demonstrate the effectiveness of our method. The clone model achieves a clean accuracy of 77.86% and a robust accuracy of 39.51% against AutoAttack, which are only 4.71% and 8.40% lower than the target model on the CIFAR-10 dataset, significantly exceeding the baselines. Our code is available at: https://github.com/LetheSec/DFHL-RS-Attack.

2023-12-10T16:14:02Z Accepted by AAAI 2024 Xiaojian Yuan Kejiang Chen Wen Huang Jie Zhang Weiming Zhang Nenghai Yu http://arxiv.org/abs/2311.04382v1 2023-11-07T23:06:22Z

This paper introduces a new mathematical and numerical framework for surface analysis derived from the general setting of elastic Riemannian metrics on shape spaces. Traditionally, those metrics are defined over the infinite dimensional manifold of immersed surfaces and satisfy specific invariance properties enabling the comparison of surfaces modulo shape preserving transformations such as reparametrizations. The specificity of the approach we develop is to restrict the space of allowable transformations to predefined finite dimensional bases of deformation fields. These are estimated in a data-driven way so as to emulate specific types of surface transformations observed in a training set. The use of such bases allows to simplify the representation of the corresponding shape space to a finite dimensional latent space. However, in sharp contrast with methods involving e.g. mesh autoencoders, the latent space is here equipped with a non-Euclidean Riemannian metric precisely inherited from the family of aforementioned elastic metrics. We demonstrate how this basis restricted model can be then effectively implemented to perform a variety of tasks on surface meshes which, importantly, does not assume these to be pre-registered (i.e. with given point correspondences) or to even have a consistent mesh structure. We specifically validate our approach on human body shape and pose data as well as human face scans, and show how it generally outperforms state-of-the-art methods on problems such as shape registration, interpolation, motion transfer or random pose generation.

2023-11-07T23:06:22Z 18 pages, 10 figures, 8 tables Emmanuel Hartman Emery Pierson Martin Bauer Mohamed Daoudi Nicolas Charon http://arxiv.org/abs/2307.13948v1 2023-07-26T04:03:10Z

Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion. In this paper, we aim to investigate the capability of reconstructing the 3D facial shape from voice from a geometry perspective without any semantic information. We propose a voice-anthropometric measurement (AM)-face paradigm, which identifies predictable facial AMs from the voice and uses them to guide 3D face reconstruction. By leveraging AMs as a proxy to link the voice and face geometry, we can eliminate the influence of unpredictable AMs and make the face geometry tractable. Our approach is evaluated on our proposed dataset with ground-truth 3D face scans and corresponding voice recordings, and we find significant correlations between voice and specific parts of the face geometry, such as the nasal cavity and cranium. Our work offers a new perspective on voice-face correlation and can serve as a good empirical study for anthropometry science.

2023-07-26T04:03:10Z ACM Multimedia 2023 Xiang Li Yandong Wen Muqiao Yang Jinglu Wang Rita Singh Bhiksha Raj http://arxiv.org/abs/2305.10132v3 2023-07-27T01:45:26Z

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately merging facial scans and CBCT images are due to the different image acquisition methods and limited area of correspondence between the two facial surfaces. In addition, it is difficult to use machine learning techniques because they use face-related 3D medical data with radiation exposure, which are difficult to obtain for training. The proposed method addresses these problems by reusing an existing machine-learning-based 2D landmark detection algorithm in an open-source library and developing a novel mathematical algorithm that identifies paired 3D landmarks from knowledge of the corresponding 2D landmarks. A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models. Note that this reduces a 3D landmark detection problem to a 2D problem of identifying the corresponding landmarks on two 2D projection images generated from two different projection angles. Here, the 3D landmarks for registration were selected from the sub-surfaces with the least geometric change under the CBCT and face scan environments. For the final fine-tuning of the registration, the Iterative Closest Point method was applied, which utilizes geometrical information around the 3D landmarks. The experimental results show that the proposed method achieved an averaged surface distance error of 0.74 mm for three pairs of CBCT and face scan datasets.

2023-05-17T11:26:43Z 8 pages, 6 figures, 2 tables Hyoung Suk Park Chang Min Hyun Sang-Hwy Lee Jin Keun Seo Kiwan Jeon 10.1109/ACCESS.2024.3431673 http://arxiv.org/abs/2304.02814v1 2023-04-06T01:32:58Z

We present a high-precision real-time facial animation pipeline suitable for animators to use on their desktops. This pipeline is about to be launched in FACEGOOD's Avatary\footnote{https://www.avatary.com/} software, which will accelerate animators' productivity. The pipeline differs from professional head-mounted facial capture solutions in that it only requires the use of a consumer-grade 3D camera on the desk to achieve high-precision real-time facial capture. The system enables animators to create high-quality facial animations with ease and speed, while reducing the cost and complexity of traditional facial capture solutions. Our approach has the potential to revolutionize the way facial animation is done in the entertainment industry.

2023-04-06T01:32:58Z 7pages, 5 figures Wei Chen HongWei Xu Jelo Wang http://arxiv.org/abs/2204.10746v2 2022-04-27T16:40:44Z

We propose an end-to-end pipeline for both building and tracking 3D facial models from personalized in-the-wild (cellphone, webcam, youtube clips, etc.) video data. First, we present a method for automatic data curation and retrieval based on a hierarchical clustering framework typical of collision detection algorithms in traditional computer graphics pipelines. Subsequently, we utilize synthetic turntables and leverage deepfake technology in order to build a synthetic multi-view stereo pipeline for appearance capture that is robust to imperfect synthetic geometry and image misalignment. The resulting model is fit with an animation rig, which is then used to track facial performances. Notably, our novel use of deepfake technology enables us to perform robust tracking of in-the-wild data using differentiable renderers despite a significant synthetic-to-real domain gap. Finally, we outline how we train a motion capture regressor, leveraging the aforementioned techniques to avoid the need for real-world ground truth data and/or a high-end calibrated camera capture setup.

2022-04-22T15:09:49Z Winnie Lin Yilin Zhu Demi Guo Ron Fedkiw http://arxiv.org/abs/2203.09824v1 2022-03-18T10:03:07Z

This work digs into a root question in human perception: can face geometry be gleaned from one's voices? Previous works that study this question only adopt developments in image synthesis and convert voices into face images to show correlations, but working on the image domain unavoidably involves predicting attributes that voices cannot hint, including facial textures, hairstyles, and backgrounds. We instead investigate the ability to reconstruct 3D faces to concentrate on only geometry, which is much more physiologically grounded. We propose our analysis framework, Cross-Modal Perceptionist, under both supervised and unsupervised learning. First, we construct a dataset, Voxceleb-3D, which extends Voxceleb and includes paired voices and face meshes, making supervised learning possible. Second, we use a knowledge distillation mechanism to study whether face geometry can still be gleaned from voices without paired voices and 3D face data under limited availability of 3D face scans. We break down the core question into four parts and perform visual and numerical analyses as responses to the core question. Our findings echo those in physiology and neuroscience about the correlation between voices and facial structures. The work provides future human-centric cross-modal learning with explainable foundations. See our project page: https://choyingw.github.io/works/Voice2Mesh/index.html

2022-03-18T10:03:07Z Accepted to CVPR 2022. Project page: https://choyingw.github.io/works/Voice2Mesh/index.html. This version supersedes arXiv:2104.10299 Cho-Ying Wu Chin-Cheng Hsu Ulrich Neumann http://arxiv.org/abs/2203.09729v2 2022-07-19T16:12:49Z

The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists of 100 globally aligned face scans with accurate facial keypoints, high-quality region masks, and topology-consistent meshes. Our approach performs region-wise shape alignment and leads to more accurate, bidirectional correspondences during computing the shape errors. The fine-grained, region-wise evaluation results provide us detailed understandings about the performance of state-of-the-art 3D face reconstruction methods. For example, our experiments on single-image based reconstruction methods reveal that DECA performs the best on nose regions, while GANFit performs better on cheek regions. Besides, a new and high-quality 3DMM basis, HIFI3D++, is further derived using the same procedure as we construct REALY to align and retopologize several 3D face datasets. We will release REALY, HIFI3D++, and our new evaluation pipeline at https://realy3dface.com.

2022-03-18T04:04:45Z Accepted to ECCV 2022, camera-ready version; Project page: https://realy3dface.com; Code: https://github.com/czh-98/REALY Zenghao Chai Haoxian Zhang Jing Ren Di Kang Zhengzhuo Xu Xuefei Zhe Chun Yuan Linchao Bao http://arxiv.org/abs/2203.05885v1 2022-03-11T12:39:08Z

Additive manufacturing can realize almost any designed geometry, enabling the fabrication of innovative products for advanced applications. Local electrochemical plating is a powerful approach for additive manufacturing of metal microstructures; however, previously reported data have been mostly obtained with copper, and only a few cases have been reported with other elements. In this study, we assessed the ability of fluidic force microscopy (FluidFM) to produce Ni-Mn and Ni-Co alloy structures. Once the optimal deposition potential window was determined, pillars with relatively smooth surfaces were obtained. The printing process was characterized by printing rates in the range of 50-60 nm/s. Cross-sections exposed by focused ion beam showed highly dense microstructures, while the corresponding face scan with energy-dispersive X-ray spectroscopy (EDX) spectra revealed a uniform distribution of alloy components.

2022-03-11T12:39:08Z Chunjian Shen Zengwei Zhu Di Zhu Cathelijn van Nisselroy Tomaso Zambelli Dmitry Momotenko 10.1088/1361-6528/ac5a80 http://arxiv.org/abs/2202.05592v4 2022-09-16T07:26:39Z

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.

2022-02-11T13:22:48Z For project page, see https://sites.google.com/view/npfa/ Notice: You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of database Longwen Zhang Chuxiao Zeng Qixuan Zhang Hongyang Lin Ruixiang Cao Wei Yang Lan Xu Jingyi Yu http://arxiv.org/abs/2112.00532v1 2021-12-01T14:42:03Z

In this paper, we present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. We propose a first adaptation of image-to-image translation networks, that have successfully been used in the 2D domain, to 3D face geometry. Leveraging recently released large face scan databases, a neural network has been trained to decouple factors of variations with a better knowledge of the face, enabling facial expressions transfer and neutralization of expressive faces. Specifically, we design an adversarial architecture adapting the base architecture of FUNIT and using SpiralNet++ for our convolutional and sampling operations. Using two publicly available datasets (FaceScape and CoMA), FaceTuneGAN has a better identity decomposition and face neutralization than state-of-the-art techniques. It also outperforms classical deformation transfer approach by predicting blendshapes closer to ground-truth data and with less of undesired artifacts due to too different facial morphologies between source and target.

2021-12-01T14:42:03Z Nicolas Olivier Kelian Baert Fabien Danieau Franck Multon Quentin Avril http://arxiv.org/abs/2111.07556v1 2021-11-15T06:42:27Z

We propose a real time deep learning framework for video-based facial expression capture. Our process uses a high-end facial capture pipeline based on FACEGOOD to capture facial expression. We train a convolutional neural network to produce high-quality continuous blendshape weight output from video training. Since this facial capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We demonstrate compelling animation inference in challenging areas such as eyes and lips.

2021-11-15T06:42:27Z arXiv admin note: text overlap with arXiv:1609.06536 by other authors Hongwei Xu Leijia Dai Jianxing Fu Xiangyuan Wang Quanwei Wang http://arxiv.org/abs/2105.05708v1 2021-05-12T14:48:39Z

Facial expressions recognition (FER) of 3D face scans has received a significant amount of attention in recent years. Most of the facial expression recognition methods have been proposed using mainly 2D images. These methods suffer from several issues like illumination changes and pose variations. Moreover, 2D mapping from 3D images may lack some geometric and topological characteristics of the face. Hence, to overcome this problem, a multi-modal 2D + 3D feature-based method is proposed. We extract shallow features from the 3D images, and deep features using Convolutional Neural Networks (CNN) from the transformed 2D images. Combining these features into a compact representation uses covariance matrices as descriptors for both features instead of single-handedly descriptors. A covariance matrix learning is used as a manifold layer to reduce the deep covariance matrices size and enhance their discrimination power while preserving their manifold structure. We then use the Bag-of-Features (BoF) paradigm to quantize the covariance matrices after flattening. Accordingly, we obtained two codebooks using shallow and deep features. The global codebook is then used to feed an SVM classifier. High classification performances have been achieved on the BU-3DFE and Bosphorus datasets compared to the state-of-the-art methods.

2021-05-12T14:48:39Z Walid Hariri Nadir Farah Dinesh Kumar Vishwakarma http://arxiv.org/abs/2104.10299v1 2021-04-21T01:14:50Z

This work focuses on the analysis that whether 3D face models can be learned from only the speech inputs of speakers. Previous works for cross-modal face synthesis study image generation from voices. However, image synthesis includes variations such as hairstyles, backgrounds, and facial textures, that are arguably irrelevant to voice or without direct studies to show correlations. We instead investigate the ability to reconstruct 3D faces to concentrate on only geometry, which is more physiologically grounded. We propose both the supervised learning and unsupervised learning frameworks. Especially we demonstrate how unsupervised learning is possible in the absence of a direct voice-to-3D-face dataset under limited availability of 3D face scans when the model is equipped with knowledge distillation. To evaluate the performance, we also propose several metrics to measure the geometric fitness of two 3D faces based on points, lines, and regions. We find that 3D face shapes can be reconstructed from voices. Experimental results suggest that 3D faces can be reconstructed from voices, and our method can improve the performance over the baseline. The best performance gains (15% - 20%) on ear-to-ear distance ratio metric (ER) coincides with the intuition that one can roughly envision whether a speaker's face is overall wider or thinner only from a person's voice. See our project page for codes and data.

2021-04-21T01:14:50Z Project page: https://choyingw.github.io/works/Voice2Mesh/index.html Cho-Ying Wu Ke Xu Chin-Cheng Hsu Ulrich Neumann http://arxiv.org/abs/2012.09235v2 2021-03-10T15:25:41Z

Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.

2020-12-16T20:02:36Z In review with International Journal of Computer Vision (IJCV) - Revision 1 (minor revision) Mehdi Bahri Eimear O' Sullivan Shunwang Gong Feng Liu Xiaoming Liu Michael M. Bronstein Stefanos Zafeiriou