Official implementation for the paper (CVPR25):
PromptHMR: Promptable Human Mesh Recovery
Yufu Wang, Yu Sun, Priyanka Patel, Kostas Daniilidis, Michael J. Black, Muhammed Kocabas
[Project Page]
[Arxiv]
dance_1.mp4
dance_2.mp4
boxing.mp4
- Clone this project.
git clone https://github.com/yufu-wang/phmr_dev- Run installation script to create a conda environment and install requirements. We provide two options: either torch==2.4.0+cu121 (pass
--pt_version=2.4) or torch==2.6.0+cu126 (pass--pt_version=2.6). (Optional) If you want to run the world-coordinate multi-human video pipeline, you will need additional thirdparty packages.
Usage: scripts/install.sh --pt_version <version> [--world-video=<true|false>]
Options:
--pt_version <version> PyTorch version to install (2.4 or 2.6)
--world-video <true|false> Download required wheels for world-coordinate multi-human video (default: false)
--help Show this help message
Examples:
scripts/install.sh --pt_version=2.4
scripts/install.sh --pt_version=2.6
scripts/install.sh --pt_version=2.4 --world-video=true
scripts/install.sh --pt_version=2.6 --world-video=falseRun the following commands to download all models and checkpoints into the data/ directory. The first command will prompt you to register and log in to access each version of SMPL.
# SMPLX family models
bash scripts/fetch_smplx.sh
# Checkpoints and annotations
bash scripts/fetch_data.shFor monocular reconstruction, the demo saves results in a new folder named after the input image. It also uses Viser to visualize the results—open the output link in a browser to view them in 3D. If the browser cannot connect, please troubleshoot Viser (e.g., port forwarding is required if you're running on a remote server).
# 1. Single view reconstruction
python scripts/demo_phmr.py --image data/examples/example_1.jpg --gravity_alignFor world-coordinate video reconstruction, first install the precompiled wheels as described in our installation guide. After installation, run the example commands below—results will be visualized using Viser.
If you're working with a long sequence containing many people, consider using --viser_total to limit the number of frames visualized, or --viser_subsample to subsample frames.
For other hyperparameters, refer to pipeline/config.yaml.
# 2. Video world-coordinate reconstruction
# Example 1: simple example
python scripts/demo_video.py --input_video data/examples/boxing.mp4
# Example 2: with static camera
python scripts/demo_video.py --input_video data/examples/dance_1.mp4 --static_camera --viser_subsample 4
# Example 3: moving camera
python scripts/demo_video.py --input_video data/examples/dance_2.mp4 --viser_subsample 3
Note that this script will output MCS and GLB files. You can drag and drop the MCS file to https://me.meshcapade.com/editor to view the results. You can import the GLB file into the Blender editor to visualize human and camera motion.
Note on Viser: If you're running the demo on a server and viewing it through a local browser, the meshes are streamed to the browser's memory, which can be slow for long sequences with many people. A potential workaround is to download the results.pkl file and modify the demo to load and visualize the results locally.
Please update the dataset directory in data_config.py, and then run the following command for pose and shape evaluation.
# Available datasets: EMDB, 3DPW_TEST, HI4D_TEST, RICH_TEST
python scripts/eval_phmr.py --dataset EMDBDue to licensing agreements, we currently do not plan to release the training code. For details related to training, please refer to the paper and its supplementary materials.
We benefit greatly from the following open source works, from which we adapted parts of our code.
- SAM: promptable architecture
- MultiHMR & BEV: multi-person baseline
- GVHMR: video head design
- CamHMR: annotations
- BUDDI: two-person interation
- viser & Gloss: visualization
In addition, the pipeline includes Detectron2, SAM2, DROID-SLAM, Metric3D, ViTPose and SPEC.
@article{wang2025prompthmr,
title={PromptHMR: Promptable Human Mesh Recovery},
author={Wang, Yufu and Sun, Yu and Patel, Priyanka and Daniilidis, Kostas and Black, Michael J and Kocabas, Muhammed},
journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2025}
}