README.md

metadata

license: mit
tags:
  - physics
  - animation
  - reinforcement-learning
  - motion-imitation
  - browser

physgait-weights

Trained locomotion / combat weights for the Ashen Depths neural souls demo (Space). These are the physics gait weights: a character that learns to move by driving a rigid-body ragdoll, rather than by playing back a mocap clip.

What the weights are

player_moves.json (physmoves-v1) stores, per move, a compact policy over an XPBD articulated ragdoll:

• 13 capsule bodies (pelvis, spine, head, upper/lower arms, upper/lower legs, feet) linked by ball joints with compliant angular motors.
• Each actuated joint carries P = 11 parameters: motor axis (x,y,z), oscillation amplitude, phase φ, bias, gain, and four balance-feedback gains that respond to pelvis tilt / angular velocity.
• Four moves are trained: walk, attack1, block, roll.

How it was trained

DeepMimic-style motion imitation on top of Mixamo reference clips, optimised with the cross-entropy method (CEM) — a gradient-free evolutionary search that fits comfortably in a browser tab:

1. A Mixamo clip is sampled to a per-phase reference pose (sampleClipTargets), including the reference pelvis height at each phase.
2. The ragdoll's joint motors track those reference joint angles; CEM learns a residual correction + feedback gains per joint so the simulated body reproduces the clip while staying balanced under gravity and contact.
3. Reward = imitation term (joint-angle match) − upright/height penalties. The key fix that killed the ~0.33 m "hopping" artifact was root-height tracking: the pelvis is unactuated, so its target height is recorded from the reference clip per phase and firmly tracked instead of left to a soft hover spring (bounce dropped to ~0.08 m, matching the real clip bob).

Training runs entirely client-side in rlphys.html (XPBD sim + CEM in physbody.js / rltrain.js). As shown above, all four moves converge in ~16 seconds to rewards ≈ walk 3.95 · attack1 3.96 · block 3.96 · roll 3.62, then export straight to player_moves.json.

Files

file	description
player_moves.json	trained physmoves-v1 policy (walk/attack1/block/roll)
training_viewer.png	the in-browser CEM trainer after a full run

Citation

If you use these weights or the Ashen Depths neural-animation work, please cite:

@misc{byrne2025physgait,
  title        = {physgait-weights: Browser-Trained Physics-Gait Weights for Neural Character Animation},
  author       = {Byrne, Dean (Quazim0t0)},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/Quazim0t0/physgait-weights}},
  note         = {Ashen Depths neural souls demo}
}

References

This work draws on ideas from the following papers:

• AMDM — Yi Shi, Jingbo Wang, Xuekun Jiang, Bingkun Lin, Bo Dai, Xue Bin Peng. Interactive Character Control with Auto-Regressive Motion Diffusion Models. ACM SIGGRAPH 2024. project · pdf
• TRACE and PACE — Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, Or Litany. Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion. CVPR 2023 (NVIDIA). pdf

@inproceedings{shi2024amdm,
  title     = {Interactive Character Control with Auto-Regressive Motion Diffusion Models},
  author    = {Shi, Yi and Wang, Jingbo and Jiang, Xuekun and Lin, Bingkun and Dai, Bo and Peng, Xue Bin},
  booktitle = {ACM SIGGRAPH 2024 Conference Proceedings},
  year      = {2024}
}

@inproceedings{rempe2023trace,
  title     = {Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion},
  author    = {Rempe, Davis and Luo, Zhengyi and Peng, Xue Bin and Yuan, Ye and Kitani, Kris and Kreis, Karsten and Fidler, Sanja and Litany, Or},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2023}
}

Related

• Neural character animator (skeleton_animator.pt, a phase+action → 20-channel MLP) that drives the player in the shipped Space — parsed in-browser by pt_loader.js.
• Space: Quazim0t0/AshenDepths