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SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Teaser

Useful links

[Homepage]      [HuggingFace]      [arXiv]      [Video]      [MMHuman3D]

News

  • [2024-03-29] An updated version of SMPLer-X-H32 is released to fix camera estimation on 3DPW-like data.
  • [2024-02-29] HuggingFace demo is online!
  • [2023-10-23] Support visualization through SMPL-X mesh overlay and add inference docker.
  • [2023-10-02] arXiv preprint is online!
  • [2023-09-28] Homepage and Video are online!
  • [2023-07-19] Pretrained models are released.
  • [2023-06-15] Training and testing code is released.

Gallery

001.gif 001.gif 001.gif
001.gif 001.gif 001.gif

Visualization

Install

conda create -n smplerx python=3.8 -y
conda activate smplerx
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch -y
pip install mmcv-full==1.7.1 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.12.0/index.html
pip install -r requirements.txt

# install mmpose
cd main/transformer_utils
pip install -v -e .
cd ../..

Docker Support (Early Stage)

docker pull wcwcw/smplerx_inference:v0.2
docker run  --gpus all -v <vid_input_folder>:/smplerx_inference/vid_input \
        -v <vid_output_folder>:/smplerx_inference/vid_output \
        wcwcw/smplerx_inference:v0.2 --vid <video_name>.mp4
# Currently any customization need to be applied to /smplerx_inference/smplerx/inference_docker.py
  • We recently developed a docker for inference at docker hub.
  • This docker image uses SMPLer-X-H32 as inference baseline and was tested at RTX3090 & WSL2 (Ubuntu 20.04).

Pretrained Models

Model Backbone #Datasets #Inst. #Params MPE Download FPS
SMPLer-X-S32 ViT-S 32 4.5M 32M 82.6 model 36.17
SMPLer-X-B32 ViT-B 32 4.5M 103M 74.3 model 33.09
SMPLer-X-L32 ViT-L 32 4.5M 327M 66.2 model 24.44
SMPLer-X-H32 ViT-H 32 4.5M 662M 63.0 model 17.47
SMPLer-X-H32* ViT-H 32 4.5M 662M 59.7 model 17.47
  • MPE (Mean Primary Error): the average of the primary errors on five benchmarks (AGORA, EgoBody, UBody, 3DPW, and EHF)
  • FPS (Frames Per Second): the average inference speed on a single Tesla V100 GPU, batch size = 1
  • SMPLer-X-H32* is the updated version of SMPLer-X-H32, which fixes the camera estimation issue on 3DPW-like data.

Preparation

The file structure should be like:

SMPLer-X/
├── common/
│   └── utils/
│       └── human_model_files/  # body model
│           ├── smpl/
│           │   ├──SMPL_NEUTRAL.pkl
│           │   ├──SMPL_MALE.pkl
│           │   └──SMPL_FEMALE.pkl
│           └── smplx/
│               ├──MANO_SMPLX_vertex_ids.pkl
│               ├──SMPL-X__FLAME_vertex_ids.npy
│               ├──SMPLX_NEUTRAL.pkl
│               ├──SMPLX_to_J14.pkl
│               ├──SMPLX_NEUTRAL.npz
│               ├──SMPLX_MALE.npz
│               └──SMPLX_FEMALE.npz
├── data/
├── main/
├── demo/  
│   ├── videos/       
│   ├── images/      
│   └── results/ 
├── pretrained_models/  # pretrained ViT-Pose, SMPLer_X and mmdet models
│   ├── mmdet/
│   │   ├──faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
│   │   └──mmdet_faster_rcnn_r50_fpn_coco.py
│   ├── smpler_x_s32.pth.tar
│   ├── smpler_x_b32.pth.tar
│   ├── smpler_x_l32.pth.tar
│   ├── smpler_x_h32.pth.tar
│   ├── vitpose_small.pth
│   ├── vitpose_base.pth
│   ├── vitpose_large.pth
│   └── vitpose_huge.pth
└── dataset/  
    ├── AGORA/       
    ├── ARCTIC/      
    ├── BEDLAM/      
    ├── Behave/      
    ├── CHI3D/       
    ├── CrowdPose/   
    ├── EgoBody/     
    ├── EHF/         
    ├── FIT3D/                
    ├── GTA_Human2/           
    ├── Human36M/             
    ├── HumanSC3D/            
    ├── InstaVariety/         
    ├── LSPET/                
    ├── MPII/                 
    ├── MPI_INF_3DHP/         
    ├── MSCOCO/               
    ├── MTP/                    
    ├── MuCo/                   
    ├── OCHuman/                
    ├── PoseTrack/                
    ├── PROX/                   
    ├── PW3D/                   
    ├── RenBody/
    ├── RICH/
    ├── SPEC/
    ├── SSP3D/
    ├── SynBody/
    ├── Talkshow/
    ├── UBody/
    ├── UP3D/
    └── preprocessed_datasets/  # HumanData files

Inference

  • Place the video for inference under SMPLer-X/demo/videos
  • Prepare the pretrained models to be used for inference under SMPLer-X/pretrained_models
  • Prepare the mmdet pretrained model and config under SMPLer-X/pretrained_models
  • Inference output will be saved in SMPLer-X/demo/results
cd main
sh slurm_inference.sh {VIDEO_FILE} {FORMAT} {FPS} {PRETRAINED_CKPT} 

# For inferencing test_video.mp4 (24FPS) with smpler_x_h32
sh slurm_inference.sh test_video mp4 24 smpler_x_h32

2D Smplx Overlay

We provide a lightweight visualization script for mesh overlay based on pyrender.

  • Use ffmpeg to split video into images
  • The visualization script takes inference results (see above) as the input.
ffmpeg -i {VIDEO_FILE} -f image2 -vf fps=30 \
        {SMPLERX INFERENCE DIR}/{VIDEO NAME (no extension)}/orig_img/%06d.jpg \
        -hide_banner  -loglevel error

cd main && python render.py \
            --data_path {SMPLERX INFERENCE DIR} --seq {VIDEO NAME} \
            --image_path {SMPLERX INFERENCE DIR}/{VIDEO NAME} \
            --render_biggest_person False

Training

cd main
sh slurm_train.sh {JOB_NAME} {NUM_GPU} {CONFIG_FILE}

# For training SMPLer-X-H32 with 16 GPUS
sh slurm_train.sh smpler_x_h32 16 config_smpler_x_h32.py
  • CONFIG_FILE is the file name under SMPLer-X/main/config
  • Logs and checkpoints will be saved to SMPLer-X/output/train_{JOB_NAME}_{DATE_TIME}

Testing

# To eval the model ../output/{TRAIN_OUTPUT_DIR}/model_dump/snapshot_{CKPT_ID}.pth.tar 
# with confing ../output/{TRAIN_OUTPUT_DIR}/code/config_base.py
cd main
sh slurm_test.sh {JOB_NAME} {NUM_GPU} {TRAIN_OUTPUT_DIR} {CKPT_ID}
  • NUM_GPU = 1 is recommended for testing
  • Logs and results will be saved to SMPLer-X/output/test_{JOB_NAME}_ep{CKPT_ID}_{TEST_DATSET}

FAQ

  • RuntimeError: Subtraction, the '-' operator, with a bool tensor is not supported. If you are trying to invert a mask, use the '~' or 'logical_not()' operator instead.

    Follow this post and modify torchgeometry

  • KeyError: 'SinePositionalEncoding is already registered in position encoding' or any other similar KeyErrors due to duplicate module registration.

    Manually add force=True to respective module registration under main/transformer_utils/mmpose/models/utils, e.g. @POSITIONAL_ENCODING.register_module(force=True) in this file

  • How do I animate my virtual characters with SMPLer-X output (like that in the demo video)?

    • We are working on that, please stay tuned! Currently, this repo supports SMPL-X estimation and a simple visualization (overlay of SMPL-X vertices).

References

Citation

@inproceedings{cai2023smplerx,
    title={{SMPLer-X}: Scaling up expressive human pose and shape estimation},
    author={Cai, Zhongang and Yin, Wanqi and Zeng, Ailing and Wei, Chen and Sun, Qingping and Yanjun, Wang and Pang, Hui En and Mei, Haiyi and Zhang, Mingyuan and Zhang, Lei and Loy, Chen Change and Yang, Lei and Liu, Ziwei},
    booktitle={Advances in Neural Information Processing Systems},
    year={2023}
}