Official Pytorch implementation of "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video", CVPR 2021

Overview

TCMR: Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video

Qualtitative result Paper teaser video
aa bb

Introduction

This repository is the official Pytorch implementation of Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video. The base codes are largely borrowed from VIBE. Find more qualitative results here.

Installation

TCMR is tested on Ubuntu 16.04 with Pytorch 1.4 and Python 3.7.10. You may need sudo privilege for the installation.

source scripts/install_pip.sh

Quick demo

  • Download the pre-trained demo TCMR and required data by below command and download SMPL layers from here (male&female) and here (neutral). Put SMPL layers (pkl files) under ${ROOT}/data/base_data/.
source scripts/get_base_data.sh
  • Run demo with options (e.g. render on plain background). See more option details in bottom lines of demo.py.
  • A video overlayed with rendered meshes will be saved in ${ROOT}/output/demo_output/.
python demo.py --vid_file demo.mp4 --gpu 0 

Results

Here I report the performance of TCMR.

table table

See our paper for more details.

Running TCMR

Download pre-processed data (except InstaVariety dataset) from here. You may also download datasets from sources and pre-process yourself. Refer to this. Put SMPL layers (pkl files) under ${ROOT}/data/base_data/.

The data directory structure should follow the below hierarchy.

${ROOT}  
|-- data  
|   |-- base_data  
|   |-- preprocessed_data  
|   |-- pretrained_models

Evaluation

  • Download pre-trained TCMR weights from here.
  • Run the evaluation code with a corresponding config file to reproduce the performance in the tables of our paper.
# dataset: 3dpw, mpii3d, h36m 
python evaluate.py --dataset 3dpw --cfg ./configs/repr_table4_3dpw_model.yaml --gpu 0 
  • You may test options such as average filtering and rendering. See the bottom lines of ${ROOT}/lib/core/config.py.
  • We checked rendering results of TCMR on 3DPW validation and test sets.

Reproduction (Training)

  • Run the training code with a corresponding config file to reproduce the performance in the tables of our paper.
# training outputs are saved in `experiments` directory
# mkdir experiments
python train.py --cfg ./configs/repr_table4_3dpw_model.yaml --gpu 0 
  • Evaluate the trained TCMR (either checkpoint.pth.tar or model_best.pth.tar) on a target dataset.
  • You may test the motion discriminator introduced in VIBE by uncommenting the codes that have exclude motion discriminator notations.
  • We do not release NeuralAnnot SMPL annotations of Human36M used in our paper yet. Thus the performance in Table 6 may be slightly different with the paper.

Reference

@InProceedings{choi2020beyond,
  title={Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video},
  author={Choi, Hongsuk and Moon, Gyeongsik and Lee, Kyoung Mu},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}
  year={2021}
}
Owner
Hongsuk Choi
Research area: 3D human pose, shape, and mesh estimation
Hongsuk Choi
Аналитика доходности инвестиционного портфеля в Тинькофф брокере

Аналитика доходности инвестиционного портфеля Тиньков Видео на YouTube Для работы скрипта нужно установить три переменных окружения: export TINKOFF_TO

Alexey Goloburdin 64 Dec 17, 2022
Code basis for the paper "Camera Condition Monitoring and Readjustment by means of Noise and Blur" (2021)

Camera Condition Monitoring and Readjustment by means of Noise and Blur This repository contains the source code of the paper: Wischow, M., Gallego, G

7 Dec 22, 2022
Classify music genre from a 10 second sound stream using a Neural Network.

MusicGenreClassification Academic research in the field of Deep Learning (Deep Neural Networks) and Sound Processing, Tel Aviv University. Featured in

Matan Lachmish 453 Dec 27, 2022
Trafffic prediction analysis using hybrid models - Machine Learning

Hybrid Machine learning Model Clone the Repository Create a new Directory as assests and download the model from the below link Model Link To Start th

1 Feb 08, 2022
DualGAN-tensorflow: tensorflow implementation of DualGAN

ICCV paper of DualGAN DualGAN: unsupervised dual learning for image-to-image translation please cite the paper, if the codes has been used for your re

Jack Yi 252 Nov 10, 2022
[ICLR2021] Unlearnable Examples: Making Personal Data Unexploitable

Unlearnable Examples Code for ICLR2021 Spotlight Paper "Unlearnable Examples: Making Personal Data Unexploitable " by Hanxun Huang, Xingjun Ma, Sarah

Hanxun Huang 98 Dec 07, 2022
PyTorch code for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning PyTorch code for our ACL 2020 paper "MART: Memory-Augmented Recur

Jie Lei 雷杰 151 Jan 06, 2023
[ICCV21] Official implementation of the "Social NCE: Contrastive Learning of Socially-aware Motion Representations" in PyTorch.

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

VITA lab at EPFL 125 Dec 23, 2022
Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

StyleCariGAN in PyTorch Official implementation of StyleCariGAN:Caricature Generation via StyleGAN Feature Map Modulation in PyTorch Requirements PyTo

PeterZhouSZ 49 Oct 31, 2022
Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

cosFormer Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention Update log 2022/2/28 Add core code License This

120 Dec 15, 2022
An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Deep Permutation Equivariant Structure from Motion Paper | Poster This repository contains an implementation for the ICCV 2021 paper Deep Permutation

72 Dec 27, 2022
Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

Progressive Transformers for End-to-End Sign Language Production Source code for "Progressive Transformers for End-to-End Sign Language Production" (B

58 Dec 21, 2022
An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification The repostiory consists of the code, results and data set links for

12 Dec 26, 2022
CKD - Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding

Collaborative Knowledge Distillation for Heterogeneous Information Network Embed

zhousheng 9 Dec 05, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Libo Qin 25 Sep 06, 2022
[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

Rui Peng 110 Dec 23, 2022
A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

24 Dec 13, 2022
A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.

CCasGNN A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics,

5 Apr 29, 2022
Gym for multi-agent reinforcement learning

PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym. Our website, with

Farama Foundation 1.6k Jan 09, 2023
Code Release for ICCV 2021 (oral), "AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds"

AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds (ICCV 2021 oral) **Project Page | Arxiv ** Runsong Zhu¹, Yuan Liu², Zhen Dong¹, Te

40 Dec 30, 2022