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

Streamlit tool to explore coco datasets

CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search

Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation

Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

Deep Learning agent of Starcraft2, similar to AlphaStar of DeepMind except size of network.

OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers (NeurIPS 2021)

Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models

Speech Recognition using DeepSpeech2.

TensorRT examples (Jetson, Python/C++)(object detection)

Minecraft Hack Detection With Python

ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis

PyTorch implementation of the end-to-end coreference resolution model with different higher-order inference methods.

Code for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators"

Diverse Image Captioning with Context-Object Split Latent Spaces (NeurIPS 2020)

Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight

An Open-Source Tool for Automatic Disease Diagnosis..

LowRankModels.jl is a julia package for modeling and fitting generalized low rank models.

This is the official implementation for the paper "Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization" in NeurIPS 2021.

Generative Art Using Neural Visual Grammars and Dual Encoders

Pipeline for employing a Lightweight deep learning models for LOW-power systems