[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

Related tags

Deep Learningdeep-learningpytorch
Overview

MAED: Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

Getting Started

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.txt

Data Preparation

Refer to data.md for instructions.

Training

Stage 1 training

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.yaml

On 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.yaml

Otherwise, 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.yaml

The checkpoint of training will be saved in [results/] by default. You are free to modify it in the config file.

Stage 2 training

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.

Evaluation

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 3dpw

Evaluation 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

Citation

@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}
}
Owner
ZiNiU WaN
[email protected];
ZiNiU WaN
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