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This repository contains the official implementation of UniPose. UniPose is a multi-modal LLM to understand, generate and edit 3D human poses. It enables seamless transformation of human poses across multi-modalities (visual, text, SMPL) and multi-tasks (comprehension, generation, editing).
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Clone this repo and navigate to UniPose folder
git clone https://github.com/liyiheng23/UniPose.git cd UniPose -
Create Conda Environment and Install Package
conda create -n unipose python=3.10 -y conda activate unipose pip3 install -r requirements.txt
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Download Pre-trained Models and put these models under
./cachefolder.CLIP-ViT: openai/clip-vit-large-patch14-336LLaVA: liuhaotian/llava-v1.6-mistral-7bsmpl_models: smplh
unipose: L-yiheng/UniPosetokenhmr_model: tokenhmr_modelpose-vqvae: pose-vqvae
Now under
./cache, you should be able to see the following:cache ├── clip-vit-large-patch14-336 ├── llava-v1.6-mistral-7b ├── pose_vqvae │ └── best_MPJPE.ckpt ├── smpl_models │ └── smplh │ ├── SMPLH_FEMALE.npz │ ├── SMPLH_MALE.npz │ └── SMPLH_NEUTRAL.npz ├── tokenhmr_model.ckpt └── unipose
python inference.py \
--model-path cache/unipose \
--model-base cache/llava-v1.6-mistral-7b \
--config configs/inference.pyThe provided code and pre-trained weights are licensed under the Apache 2.0 license.
This code is based on ChatPose, LISA, LLaVA and PoseScript. Some code are brought from TokenHMR, 4D-Humans. We thank the authors for their awesome work.
If you have any questions, please feel free to reach me out at [email protected].
If you find our work useful for your research, please consider citing our paper:
@article{li2024unipose,
title={UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing},
author={Li, Yiheng and Hou, Ruibing and Chang, Hong and Shan, Shiguang and Chen, Xilin},
journal={arXiv preprint arXiv:2411.16781},
year={2024}
}