Coral79/frankenmotion-eval-model

FrankenMotion — Per-body-part Evaluation Encoders

Per-body-part TMR-style retrieval encoders used to evaluate motion generation in the CVPR 2026 paper FrankenMotion: Part-level Human Motion Generation and Composition.

• 📄 Paper: https://huggingface.co/papers/2601.10909
• 💻 Code: https://github.com/Coral79/FrankenMotion-Code
• 🤖 Diffusion model: Coral79/frankenmotion
• 🧟 Dataset: Coral79/frankenstein-dataset

What's inside

Nine independently trained TMR encoders (one per body part + caption + action), each pairing a motion encoder with a text encoder into a shared retrieval latent space:

action/, head/, left_arm/, left_leg/, right_arm/, right_leg/,
sequence_caption/, spine/, trajectory/
    ├── config.json
    └── last_weights/{motion,text}_encoder.pt
stats/{mean,std}.pt          # shared motion-feature normaliser stats

These encoders feed the guo and guo+threshold retrieval protocols (R@1, R@3, MM-Dist, FID, Diversity) reported in paper Table 1.

Quick start

from huggingface_hub import snapshot_download
snapshot_download(repo_id="Coral79/frankenmotion-eval-model", local_dir="pretrained/eval_model")

Then follow the main repo README — src.eval.paper_table auto-runs both retrieval protocols on CPU and reproduces the paper table.

Citation

@inproceedings{li2026frankenmotion,
  title={{FrankenMotion}: Part-level Human Motion Generation and Composition},
  author={Li, Chuqiao and Xie, Xianghui and Cao, Yong and Geiger, Andreas and Pons-Moll, Gerard},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}