Awesome
One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer
Project Page | Video | Paper | Data
Authors
Jing Lin, Ailing Zeng, Haoqian Wang, Lei Zhang, Yu Li
<p align="middle"> <img src="assets/demo_video.gif" width="1000"> <br> <em>The proposed UBody dataset</em> </p>News
- 2024.08.26 : Update the implementation of the re-projection from SMPL-X to whole-body 2d keypoints (e.g., to align the 3D-to-2D keypoints), please check 3. Quick demo [Update information], Thanks to Yuhang Yang.
- 2023.10.12 : UBody is now supported in MMPose. Please feel free to use it. ð
- 2023.07.28 : UBody can boost 2D whole-body pose estimation and controllable image generation, especially for in-the-wild hand keypoint detection. The training and test code and pre-trained models are released. See details. ð¥³
- 2023.05.03 : UBody-V1 is released. We'll release UBody-V2 later, which have manually annotated bboxes. :man_dancing:
- 2023.04.17 : We fix bug of rendering in A100/V100 and support yolov5 as a person detector in demo.py. :rocket:
- 2023.04.15 : We merge OSX into Grounded-SAM and support promptable 3D whole-body mesh recovery. ð¥
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A person with pink clothes |
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A man with a sunglasses |
1. Introduction
This repo is official PyTorch implementation of One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer (CVPR2023). We propose the first one-stage whole-body mesh recovery method (OSX) and build a large-scale upper-body dataset (UBody). It is the top-1 method on AGORA benchmark SMPL-X Leaderboard (dated March 2023).
2. Create Environment
-
Recommend to install by:
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
-
Python packages:
bash install.sh
3. Quick demo
- Download the pre-trained OSX from here.
- Prepare pre-trained snapshot at
pretrained_models
folder. - Prepare
human_model_files
folder following belowDirectory
part and place it atcommon/utils/human_model_files
. - Go to
demo
folders, and runpython demo.py --gpu 0 --img_path IMG_PATH --output_folder OUTPUT_FOLDER
. Please replaceIMG_PATH
andOUTPUT_FOLDRE
with your own image path and saving folder. For a more efficient inference, you can add--decoder_setting wo_decoder --pretrained_model_path ../pretrained_models/osx_l_wo_decoder.pth.tar
to use the encoder-only version OSX. - [Update information] The inference code will output the projected 2d kpts with shape (137,2), please refer to here for details of these 137 key points. The SMPLX version we use has 144 joints, please refer to this line. if you want to use COCO format key points, please refer to here. Note: the key points projected onto the image may be misaligned with humans, this is likely due to inaccurate boxes provided by detection models. It is recommended to use more advanced detection models or manually set bounding boxes.
- If you run this code in ssh environment without display device, do follow:
1ãInstall oemesa follow https://pyrender.readthedocs.io/en/latest/install/
2ãReinstall the specific pyopengl fork: https://github.com/mmatl/pyopengl
3ãSet opengl's backend to egl or osmesa via os.environ["PYOPENGL_PLATFORM"] = "egl"
4. Directory
(1) Root
The ${ROOT}
is described as below.
${ROOT}
|-- data
|-- dataset
|-- demo
|-- main
|-- pretrained_models
|-- tool
|-- output
|-- common
| |-- utils
| | |-- human_model_files
| | | |-- 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
| | | |-- mano
| | | | |-- MANO_LEFT.pkl
| | | | |-- MANO_RIGHT.pkl
| | | |-- flame
| | | | |-- flame_dynamic_embedding.npy
| | | | |-- flame_static_embedding.pkl
| | | | |-- FLAME_NEUTRAL.pkl
data
contains data loading codes.dataset
contains soft links to images and annotations directories.pretrained_models
contains pretrained models.demo
contains demo codes.main
contains high-level codes for training or testing the network.tool
contains pre-processing codes of AGORA and pytorch model editing codes.output
contains log, trained models, visualized outputs, and test result.common
contains kernel codes for Hand4Whole.human_model_files
containssmpl
,smplx
,mano
, andflame
3D model files. Download the files from [smpl] [smplx] [SMPLX_to_J14.pkl] [mano] [flame]. We provide the download links for each file here.
(2) Data
You need to follow directory structure of the dataset
as below.
${ROOT}
|-- dataset
| |-- AGORA
| | |-- data
| | | |-- AGORA_train.json
| | | |-- AGORA_validation.json
| | | |-- AGORA_test_bbox.json
| | | |-- 1280x720
| | | |-- 3840x2160
| |-- EHF
| | |-- data
| | | |-- EHF.json
| |-- Human36M
| | |-- images
| | |-- annotations
| |-- MPII
| | |-- data
| | | |-- images
| | | |-- annotations
| |-- MPI_INF_3DHP
| | |-- data
| | | |-- images_1k
| | | |-- MPI-INF-3DHP_1k.json
| | | |-- MPI-INF-3DHP_camera_1k.json
| | | |-- MPI-INF-3DHP_joint_3d.json
| | | |-- MPI-INF-3DHP_SMPL_NeuralAnnot.json
| |-- MSCOCO
| | |-- images
| | | |-- train2017
| | | |-- val2017
| | |-- annotations
| |-- PW3D
| | |-- data
| | | |-- 3DPW_train.json
| | | |-- 3DPW_validation.json
| | | |-- 3DPW_test.json
| | |-- imageFiles
| |-- UBody
| | |-- images
| | |-- videos
| | |-- annotations
| | |-- splits
| | | |-- inter_scene_test_list.npy
| | | |-- intra_scene_test_list.npy
-
Download AGORA parsed data [data][parsing codes]
-
Download EHF parsed data [data]
-
Download Human3.6M parsed data and SMPL-X parameters [data][SMPL-X parameters from NeuralAnnot]
-
Download MPII parsed data and SMPL-X parameters [data][SMPL-X parameters from NeuralAnnot]
-
Download MPI-INF-3DHP parsed data and SMPL-X parameters [data][SMPL-X parameters from NeuralAnnot]
-
Download MSCOCO data and SMPL-X parameters [data][SMPL-X parameters]
-
Download 3DPW parsed data [data]
-
Download UBody dataset from [data] and run the following commond to convert the videos into images:
cd tool/UBody python video2image.py
-
All annotation files follow MSCOCO format. If you want to add your own dataset, you have to convert it to MSCOCO format.
(3) Output
You need to follow the directory structure of the output
folder as below.
${ROOT}
|-- output
| |-- log
| |-- model_dump
| |-- result
| |-- vis
- Creating
output
folder as soft link form is recommended instead of folder form because it would take large storage capacity. log
folder contains training log file.model_dump
folder contains saved checkpoints for each epoch.result
folder contains final estimation files generated in the testing stage.vis
folder contains visualized results.
5. Training OSX
(1) Download Pretrained Encoder
Download pretrained encoder osx_vit_l.pth
and osx_vit_b.pth
from here and place the pretrained model to pretrained_models/
.
(2) Setting1: Train on MSCOCO, Human3.6m, MPII and Test on EHF and AGORA-val
In the main
folder, run
python train.py --gpu 0,1,2,3 --lr 1e-4 --exp_name output/train_setting1 --end_epoch 14 --train_batch_size 16
After training, run the following command to evaluate your pretrained model on EHF and AGORA-val:
# test on EHF
python test.py --gpu 0,1,2,3 --exp_name output/train_setting1/ --pretrained_model_path ../output/train_setting1/model_dump/snapshot_13.pth.tar --testset EHF
# test on AGORA-val
python test.py --gpu 0,1,2,3 --exp_name output/train_setting1/ --pretrained_model_path ../output/train_setting1/model_dump/snapshot_13.pth.tar --testset AGORA
To speed up, you can use a light-weight version OSX by change the encoder setting by adding --encoder_setting osx_b
or change the decoder setting by adding --decoder_setting wo_face_decoder
. We recommend adding --decoder_setting wo_face_decoder
as it would obviously speed up and would not lead to significant performance decline. It takes about 20 hours to finish the training with one NVIDIA A100.
(3) Setting2: Train on AGORA and Test on AGORA-test
In the main
folder, run
python train.py --gpu 0,1,2,3 --lr 1e-4 --exp_name output/train_setting2 --end_epoch 140 --train_batch_size 16 --agora_benchmark --decoder_setting wo_decoder
After training, run the following command to evaluate your pretrained model on AGORA-test:
python test.py --gpu 0,1,2,3 --exp_name output/train_setting2/ --pretrained_model_path ../output/train_setting2/model_dump/snapshot_139.pth.tar --testset AGORA --agora_benchmark --test_batch_size 64 --decoder_setting wo_decoder
The reconstruction result will be saved at output/train_setting2/result/
.
You can zip the predictions
folder into predictions.zip
and submit it to the AGORA benchmark to obtain the evaluation metrics.
You can use a light-weight version OSX by adding --encoder_setting osx_b
.
(4) Setting3: Train on MSCOCO, Human3.6m, MPII, UBody-Train and Test on UBody-val
In the main
folder, run
python train.py --gpu 0,1,2,3 --lr 1e-4 --exp_name output/train_setting3 --train_batch_size 16 --ubody_benchmark --decoder_setting wo_decoder
After training, run the following command to evaluate your pretrained model on UBody-test:
python test.py --gpu 0,1,2,3 --exp_name output/train_setting3/ --pretrained_model_path ../output/train_setting3/model_dump/snapshot_13.pth --testset UBody --test_batch_size 64 --decoder_setting wo_decoder
The reconstruction result will be saved at output/train_setting3/result/
.
6. Testing OSX
(1) Download Pretrained Models
Download pretrained models osx_l.pth.tar
and osx_l_agora.pth.tar
from here and place the pretrained model to pretrained_models/
.
(2) Test on EHF
In the main
folder, run
python test.py --gpu 0,1,2,3 --exp_name output/test_setting1 --pretrained_model_path ../pretrained_models/osx_l.pth.tar --testset EHF
(3) Test on AGORA-val
In the main
folder, run
python test.py --gpu 0,1,2,3 --exp_name output/test_setting1 --pretrained_model_path ../pretrained_models/osx_l.pth.tar --testset AGORA
(4) Test on AGORA-test
In the main
folder, run
python test.py --gpu 0,1,2,3 --exp_name output/test_setting2 --pretrained_model_path ../pretrained_models/osx_l_agora.pth.tar --testset AGORA --agora_benchmark --test_batch_size 64
The reconstruction result will be saved at output/test_setting2/result/
.
You can zip the predictions
folder into predictions.zip
and submit it to the AGORA benchmark to obtain the evaluation metrics.
(5) Test on UBody-test
In the main
folder, run
python test.py --gpu 0,1,2,3 --exp_name output/test_setting3 --pretrained_model_path ../pretrained_models/osx_l_wo_decoder.pth.tar --testset UBody --test_batch_size 64
The reconstruction result will be saved at output/test_setting3/result/
.
7. Results
(1) AGORA test set
<img src="./assets/agora_test.png" alt="image-20230327202353903" style="zoom: 33%;" />(2) AGORA-val, EHF, 3DPW
<img src="./assets/ehf_3dpw.png" alt="image-20230327202755593" style="zoom:67%;" /> <img src="./assets/agora_val.png" alt="image-20230327204220453" style="zoom:67%;" />Troubleshoots
-
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.
: Go to here -
TypeError: startswith first arg must be bytes or a tuple of bytes, not str.
: Go to here.
Acknowledgement
This repo is mainly based on Hand4Whole. We thank the well-organized code and patient answers of Gyeongsik Moon in the issue!
Reference
@inproceedings{lin2023one,
title={One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer},
author={Lin, Jing and Zeng, Ailing and Wang, Haoqian and Zhang, Lei and Li, Yu},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={21159--21168},
year={2023}
}