[BMVC2021] "TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation"

Last update: Dec 23, 2022

Overview

TransFusion-Pose

TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation
Haoyu Ma, Liangjian Chen, Deying Kong, Zhe Wang, Xingwei Liu, Hao Tang, Xiangyi Yan, Yusheng Xie, Shih-Yao Lin and Xiaohui Xie
In BMVC 2021
[Paper] [Video]

Overview

We propose the TransFusion, which apply the transformer architecture to multi-view 3D human pose estimation
We propose the Epipolar Field, a novel and more general form of epipolar line. It readily integrates with the transformer through our proposed geometry positional encoding to encode the 3D relationships among different views.
Extensive experiments are conducted to demonstrate that our TransFusion outperforms previous fusion methods on both Human 3.6M and SkiPose datasets, but requires substantially fewer parameters.

Installation

Clone this repo, and we'll call the directory that you cloned multiview-pose as ${POSE_ROOT}

git clone https://github.com/HowieMa/TransFusion-Pose.git

Install dependencies.

pip install -r requirements.txt

Download TransPose models pretrained on COCO.

wget https://github.com/yangsenius/TransPose/releases/download/Hub/tp_r_256x192_enc3_d256_h1024_mh8.pth

You can also download it from the official website of TransPose

Please download them under ${POSE_ROOT}/models, and make them look like this:

${POSE_ROOT}/models
└── pytorch
    └── coco
        └── tp_r_256x192_enc3_d256_h1024_mh8.pth

Data preparation

Human 3.6M

For Human36M data, please follow H36M-Toolbox to prepare images and annotations.

Ski-Pose

For Ski-Pose, please follow the instruction from their website to obtain the dataset.
Once you download the Ski-PosePTZ-CameraDataset-png.zip and ski_centers.csv, unzip them and put into the same folder, named as ${SKI_ROOT}.
Run python data/preprocess_skipose.py ${SKI_ROOT} to format it.

Your folder should look like this:

${POSE_ROOT}
|-- data
|-- |-- h36m
    |-- |-- annot
        |   |-- h36m_train.pkl
        |   |-- h36m_validation.pkl
        |-- images
            |-- s_01_act_02_subact_01_ca_01 
            |-- s_01_act_02_subact_01_ca_02

|-- |-- preprocess_skipose.py
|-- |-- skipose  
    |-- |-- annot
        |   |-- ski_train.pkl
        |   |-- ski_validation.pkl
        |-- images
            |-- seq_103 
            |-- seq_103

Training and Testing

Human 3.6M

# Training
python run/pose2d/train.py --cfg experiments-local/h36m/transpose/256_fusion_enc3_GPE.yaml --gpus 0,1,2,3

# Evaluation (2D)
python run/pose2d/valid.py --cfg experiments-local/h36m/transpose/256_fusion_enc3_GPE.yaml --gpus 0,1,2,3  

# Evaluation (3D)
python run/pose3d/estimate_tri.py --cfg experiments-local/h36m/transpose/256_fusion_enc3_GPE.yaml

Ski-Pose

# Training
python run/pose2d/train.py --cfg experiments-local/skipose/transpose/256_fusion_enc3_GPE.yaml --gpus 0,1,2,3

# Evaluation (2D)
python run/pose2d/valid.py --cfg experiments-local/skipose/transpose/256_fusion_enc3_GPE.yaml --gpus 0,1,2,3

# Evaluation (3D)
python run/pose3d/estimate_tri.py --cfg experiments-local/skipose/transpose/256_fusion_enc3_GPE.yaml

Our trained models can be downloaded from here

Citation

If you find our code helps your research, please cite the paper:

@inproceedings{ma2021transfusion,
  title={TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation},
  author={Ma, Haoyu and Chen, Liangjian and Kong, Deying and Wang, Zhe and Liu, Xingwei and Tang, Hao and Yan, Xiangyi and Xie, Yusheng and Lin, Shih-Yao and Xie, Xiaohui},
  booktitle={British Machine Vision Conference},
  year={2021}
}

[BMVC2021] "TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation"

Related tags

Overview

TransFusion-Pose

Overview

Installation

Data preparation

Human 3.6M

Ski-Pose

Training and Testing

Human 3.6M

Ski-Pose

Citation

Acknowledgement

Owner

Haoyu Ma

Explaining Hyperparameter Optimization via PDPs

Robust Partial Matching for Person Search in the Wild

Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection".

Code for the paper Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

Code release for Local Light Field Fusion at SIGGRAPH 2019

Predicting Event Memorability from Contextual Visual Semantics

Credit fraud detection in Python using a Jupyter Notebook

Reinforcement learning library in JAX.

Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch]

Code release for DS-NeRF (Depth-supervised Neural Radiance Fields)

Contrastive Learning Inverts the Data Generating Process

GeoTransformer - Geometric Transformer for Fast and Robust Point Cloud Registration

基于YoloX目标检测+DeepSort算法实现多目标追踪Baseline

Source-to-Source Debuggable Derivatives in Pure Python

Cluttered MNIST Dataset

torchbearer: A model fitting library for PyTorch

Using pretrained language models for biomedical knowledge graph completion.