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Running on Zero
Running on Zero
| title: PoseShield Collision Fix | |
| emoji: 🛡️ | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 6.15.1 | |
| app_file: app.py | |
| short_description: Fix self-collisions in SMPL-H poses via neural fields | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| ## PoseShield: Neural Collision Fields for Human Self-Collision Resolution | |
| This Space demonstrates PoseShield, a post-hoc self-collision resolver for SMPL-H poses. | |
| Given a self-intersecting pose, it uses a learned neural collision field as a differentiable | |
| constraint and solves a constrained optimization (SLSQP) to find a nearby collision-free pose. | |
| ### Usage | |
| 1. Upload a `.pkl` file containing a SMPL-H pose with a `pose` key (21 joints × 3 axis-angle rotations, shape `(63,)`), or try one of the provided example poses. | |
| 2. Click "Resolve Collisions" to run the optimization. | |
| 3. View the before/after 3D mesh visualizations and optimization metrics. | |
| ### References | |
| - [Paper (arXiv:2606.29686)](https://arxiv.org/abs/2606.29686) | |
| - [GitHub](https://github.com/lzhyu/PoseShield) | |
| - [Model Weights](https://huggingface.co/ZYYY99/PoseShield) |