nx_pose_estimate.nx
buildroot/runtime/nx_pose_estimate.nx
about
nx_pose_estimate.nx -- LEARNED pose localization from an image (roadmap R2 v0, the video->POSE keystone gap).
Sovereign + deterministic, the text2motion playbook: a silhouette (z-buffer occupancy grid) -> a PERCEPTRON
that classifies WHERE a keypoint is (a coarse region). Trained on rendered rig poses (we generate the data),
proven by HELD-OUT generalization. Honest ceiling: coarse region-classification of ONE joint on OUR-render
silhouettes; SOTA = precise full-body keypoints from real video via a trained CNN (OpenPose/MoveNet). The
architecture (image feature -> learned classifier -> keypoint) is the real thing at small scale. ORIGINAL
dependencies 1 imports · 2 importers
imports: nx_syscalls.nx
imported by: nx_pose_estimate_min.nxnx_poseest_gate.nx
structs
| none |
consts
| 8 | const K_MAGIC_1900000000: i64 = 1900000000 |
functions
| 11 | func pe_occupancy(zb: *i64, w: i64, h: i64, gw: i64, gh: i64, occ: *i64) -> i64 called by 1: pe_sample |
| 35 | func pe_score(W: *i64, r: i64, occ: *i64, nfeat: i64) -> i64 called by 1: pe_predict |
| 40 | func pe_predict(W: *i64, occ: *i64, nfeat: i64, nr: i64) -> i64 |
| 48 | func pe_train(W: *i64, occs: *i64, labels: *i64, nsamp: i64, nfeat: i64, nr: i64, epochs: i64) -> i64 |