zirobtc/vr-hmr
By zirobtc · ↓ 0 · ♥ 0 · Updated 2026-03-03
What's in this repo
This page lists the files in zirobtc/vr-hmr, including dw-ll_ucoco_384.onnx.
- 1 ONNX models (.onnx files)
- 857 files, including 334 .py, 230 .png, 87 .yaml, 86 .pt, 13 .json, 13 .txt
- Repository size ~39.4 GB
- Last updated 2026-03-03
ONNX files
| File | Size | Download |
|---|---|---|
| third_party/GVHMR/inputs/checkpoints/dwpose/dw-ll_ucoco_384.onnx | 128 MB | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
GEM: A Generalist Model for Human Motion
[Jiefeng Li](https://jeffli.site/)
·
[Jinkun Cao](https://www.jinkuncao.com/)
·
[Haotian Zhang](https://cs.stanford.edu/~haotianz/)
·
[Davis Rempe](https://davrempe.github.io/)
·
[Jan Kautz](https://jankautz.com/)
·
[Umar Iqbal](https://www.umariqbal.info/)
·
[Ye Yuan](https://ye-yuan.com/)
ICCV 2025 (Highlight)

GEM is a generalist model for human motion that handles multiple tasks with a single model, supporting diverse conditioning signals including video, keypoints, text, audio, and 3D keyframes.
📰 News
- [December 2025] 📢 GENMO has been renamed to GEM.
- [October 2025] 📢 The GEM codebase is released!
Stay tuned for the pretrained models and evaluation scripts.
Follow the project page for updates and announcements.
🚀 Highlights
GEM introduces a unified generative framework that connects motion estimation and generation through shared objectives.
- Unified framework: Reframes motion estimation as constrained generation, allowing a single model to perform both tasks.
- Regression × Diffusion synergy: Combines the accuracy of regression models with the diversity of diffusion-based generation.
- Estimation-guided training: Trains effectively on in-the-wild datasets using only 2D or textual supervision.
- Multimodal conditioning: Supports video, text, audio, 2D/3D keyframes, or even time-varying mixed inputs (e.g., video → text → video).
- Arbitrary-length motion: Generates continuous, coherent sequences of any duration in one diffusion pass.
- State-of-the-art performance: Achieves leading results on diverse motion estimation and generation benchmarks.
For more details, visit the GEM project page →
Pretrained Models
You can download pretrained models from Google Drive.
📖 Paper & Citation
Paper:
GENMO: A GENeralist Model for Human MOtion
Jiefeng Li, Jinkun Cao, Haotian Zhang, Davis Rempe, Jan Kautz, Umar Iqbal, Ye Yuan
ICCV, 2025
BibTeX:
@inproceedings{genmo2025,
title = {GENMO: A GENeralist Model for Human MOtion},
author = {Li, Jiefeng and Cao, Jinkun and Zhang, Haotian and Rempe, Davis and Kautz, Jan and Iqbal, Umar and Yuan, Ye},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2025}
}
Model info
Created: 2026-02-28
Updated: 2026-03-03
View on Hugging FaceFiles (857)
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