https://arxiv.org/api/JRdFd5FAEIY2omGIPwIxsw5L2JQ 2026-09-18T21:02:07Z 3 3 0 http://arxiv.org/abs/2403.16207v2 2024-12-22T04:04:02Z

Deducing the 3D face from a skull is a challenging task in forensic science and archaeology. This paper proposes an end-to-end 3D face reconstruction pipeline and an exploration method that can conveniently create textured, realistic faces that match the given skull. To this end, we propose a tissue-guided face creation and adaptation scheme. With the help of the state-of-the-art text-to-image diffusion model and parametric face model, we first generate an initial reference 3D face, whose biological profile aligns with the given skull. Then, with the help of tissue thickness distribution, we modify these initial faces to match the skull through a latent optimization process. The joint distribution of tissue thickness is learned on a set of skull landmarks using a collection of scanned skull-face pairs. We also develop an efficient face adaptation tool to allow users to interactively adjust tissue thickness either globally or at local regions to explore different plausible faces. Experiments conducted on a real skull-face dataset demonstrated the effectiveness of our proposed pipeline in terms of reconstruction accuracy, diversity, and stability. Our project page is https://xmlyqing00.github.io/skull-to-face-page.

2024-03-24T16:03:27Z This paper was accepted by IEEE Transactions on Visualization and Computer Graphics. Project page: https://xmlyqing00.github.io/skull-to-face-page Yongqing Liang Congyi Zhang Junli Zhao Wenping Wang Xin Li http://arxiv.org/abs/2505.10755v3 2025-10-28T11:05:00Z

We introduce Infinigen-Articulated, a toolkit for generating realistic, procedurally generated articulated assets for robotics simulation. We include procedural generators for 18 common articulated object categories along with high-level utilities for use creating custom articulated assets in Blender. We also provide an export pipeline to integrate the resulting assets along with their physical properties into common robotics simulators. Experiments demonstrate that assets sampled from these generators are effective for movable object segmentation, training generalizable reinforcement learning policies, and sim-to-real transfer of imitation learning policies.

2025-05-15T23:47:58Z Updated to include information on newly implemented assets, new experimental results (both simulation and real world), and additional features including material and dynamics parameters Abhishek Joshi Beining Han Jack Nugent Max Gonzalez Saez-Diez Yiming Zuo Jonathan Liu Hongyu Wen Stamatis Alexandropoulos Karhan Kayan Anna Calveri Tao Sun Gaowen Liu Yi Shao Alexander Raistrick Jia Deng http://arxiv.org/abs/2602.21100v3 2026-03-27T15:07:45Z

Reconstructing high-fidelity 3D head geometry from images is critical for a wide range of applications, yet existing methods face fundamental limitations. Traditional photogrammetry achieves exceptional detail but requires extensive camera arrays (25-200+ views), substantial computation, and manual cleanup in challenging areas like facial hair. Recent alternatives present a fundamental trade-off: foundation models enable efficient single-image reconstruction but lack fine geometric detail, while optimization-based methods achieve higher fidelity but require dense views and expensive computation. We bridge this gap with a hybrid approach that combines the strengths of both paradigms. Our method introduces a multi-view surface normal prediction model that extends monocular foundation models with cross-view attention to produce geometrically consistent normals in a feed-forward pass. We then leverage these predictions as strong geometric priors within an inverse rendering optimization framework to recover high-frequency surface details. Our approach outperforms state-of-the-art single-image and multi-view methods, achieving high-fidelity reconstruction on par with dense-view photogrammetry while reducing camera requirements and computational cost.

2026-02-24T17:02:11Z For our project page, see https://ubisoft-laforge.github.io/character/skullptor/ Noé Artru Rukhshanda Hussain Emeline Got Alexandre Messier David B. Lindell Abdallah Dib