Our codes are implemented and tested with python 3.6 and pytorch 1.5.
Install Pytorch following the official guide on Pytorch website.
And install the requirements using virtualenv or conda:
pip install -r requirements.txtRefer to data.md for instructions.
Generally, you can use the distributed launch script of pytorch to start training.
For example, for a training on 2 nodes, 4 gpus each (2x4=8 gpus total): On node 0, run:
python -u -m torch.distributed.launch \
--nnodes=2 \
--node_rank=0 \
--nproc_per_node=4 \
--master_port=<MASTER_PORT> \
--master_addr=<MASTER_NODE_ID> \
--use_env \
train.py --cfg configs/config_stage1.yamlOn node 1, run:
python -u -m torch.distributed.launch \
--nnodes=2 \
--node_rank=1 \
--nproc_per_node=4 \
--master_port=<MASTER_PORT> \
--master_addr=<MASTER_NODE_ID> \
--use_env \
train.py --cfg configs/config_stage1.yamlOtherwise, if you are using task scheduling system such as Slurm to submit your training tasks, you can refer to this script to start your training:
# training on 2 nodes, 4 gpus each (2x4=8 gpus total)
sh scripts/run.sh 2 4 configs/config_stage1.yamlThe checkpoint of training will be saved in [results/] by default. You are free to modify it in the config file.
Use the last checkpoint of stage 1 to initialize the model and starts training stage 2.
# On Node 0.
python -u -m torch.distributed.launch \
--nnodes=2 \
--node_rank=0 \
--nproc_per_node=4 \
--master_port=<MASTER_PORT> \
--master_addr=<MASTER_NODE_ID> \
--use_env \
train.py --cfg configs/config_stage2.yaml --pretrained <PATH_TO_CHECKPOINT_FILE>Similar on node 1.
To evaluate model on 3dpw test set:
python eval.py --cfg <PATH_TO_EXPERIMENT>/config.yaml --checkpoint <PATH_TO_EXPERIMENT>/model_best.pth.tar --eval_set 3dpwEvaluation metric is Procrustes Aligned Mean Per Joint Position Error (PA-MPJPE) in mm.
| Models | PA-MPJPE ↓ | MPJPE ↓ | PVE ↓ | ACCEL ↓ |
|---|---|---|---|---|
| HMR (w/o 3DPW) | 81.3 | 130.0 | - | 37.4 |
| SPIN (w/o 3DPW) | 59.2 | 96.9 | 116.4 | 29.8 |
| MEVA (w/ 3DPW) | 54.7 | 86.9 | - | 11.6 |
| VIBE (w/o 3DPW) | 56.5 | 93.5 | 113.4 | 27.1 |
| VIBE (w/ 3DPW) | 51.9 | 82.9 | 99.1 | 23.4 |
| ours (w/o 3DPW) | 50.7 | 88.8 | 104.5 | 18.0 |
| ours (w/ 3DPW) | 45.7 | 79.1 | 92.6 | 17.6 |
@inproceedings{wan2021,
title={Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation},
author={Ziniu Wan, Zhengjia Li, Maoqing Tian, Jianbo Liu, Shuai Yi, Hongsheng Li},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
year = {2021}
}