PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Last update: Oct 26, 2022

Overview

Unsupervised_IEPGAN

This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.
Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, Guoying Zhao.

Citation

If you use our code or paper, please consider citing:

@inproceedings{chen2021IEPGAN,
  title={Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer},
  author={Chen, Haoyu and Tang, Hao and Henglin, Shi and Peng, Wei and Sebe, Nicu and Zhao, Guoying},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}

Dependencies

Requirements:

python3.6
numpy
pytorch==1.1.0 and above
trimesh

Dataset preparation

Please download FAUST data from FAUST link, and DFAUST dataset from DFAUST link for training the model.

Usage

The usage of our code is easy, just run the code below.

bash run.sh

Your can change the hyperparameters according to your GPU settings and needs in the run.sh file:

python  train.py --batch 4 ./dataset_3d/FAUST/  --edge_loss_setting 'rec_rec' --experi_path 'full_adap_GIH_Lap_rec' --server 'local' --rec_epoch 1200 --geo_epoch 1600 --iter 2000 --geoloss 0.005 --n_crop 1 --ref_crop 2 --sampling_number 120 --limb_n 4 --limb_sampling 600  --sampling_pattern 'adaptive'

Acknowledgement

Part of our code is based on

3D transfer: NPT，

GAN framework: SWAP-Autoencoder (unofficial implementation) and

GIH implementation: LIMP. Many thanks!

License

MIT-2.0 License

PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Related tags

Overview

Unsupervised_IEPGAN

Citation

Dependencies

Dataset preparation

Usage

Acknowledgement

License

Owner

Open standard for machine learning interoperability

Data visualization app for H&M competition in kaggle

[ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.

BBB streaming without Xorg and Pulseaudio and Chromium and other nonsense (heavily WIP)

Build a small, 3 domain internet using Github pages and Wikipedia and construct a crawler to crawl, render, and index.

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

🛰️ Awesome Satellite Imagery Datasets

Code for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in Deep Latent Space"

Config files for my GitHub profile.

Constraint-based geometry sketcher for blender

SCNet: Learning Semantic Correspondence

This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language Models"

Python wrapper to access the amazon selling partner API

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Python scripts using the Mediapipe models for Halloween.

This is the PyTorch implementation of GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation

Official PyTorch implementation for paper "Efficient Two-Stage Detection of Human–Object Interactions with a Novel Unary–Pairwise Transformer"

Real-Time Multi-Contact Model Predictive Control via ADMM

Efficient Speech Processing Tookit for Automatic Speaker Recognition