3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos

Overview

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos

This repository contains the source code and dataset for the paper 3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos by Zipeng Ye, Mengfei Xia, Yanan Sun, Ran Yi, Minjing Yu, Juyong Zhang, Yu-Kun Lai and Yong-Jin Liu, which is accepted by IEEE Transactions on Visualization and Computer Graphics (TVCG).

This repository contains two parts: dataset and source code.

2D and 3D Caricature Dataset

2D Caricature Dataset

2d_dataset

We collect 5,343 hand-drawn portrait caricature images from Pinterest.com and WebCaricature dataset with facial landmarks extracted by a landmark detector, followed by human interaction for correction if needed.

The 2D dataset is in cari_2D_dataset.zip file.

3D Caricature Dataset

3d_dataset

We use the method to generate 5,343 3D caricature meshes of the same topology. We align the pose of the generated 3D caricature meshes with the pose of a template 3D head using an ICP method, where we use 5 key landmarks in eyes, nose and mouth as the landmarks for ICP. We normalize the coordinates of the 3D caricature mesh vertices by translating the center of meshes to the origin and scaling them to the same size.

The 3D dataset is in cari_3D_dataset.zip file.

3DCariPCA

We use the 3D caricature dataset to build a PCA model. We use sklearn.decomposition.PCA to build 3DCariPCA. The PCA model is pca200_icp.model file. You could use joblib to load the model and use it.

Download

You can download the two datasets and PCA in google drive and BaiduYun (code: 3kz8).

Source Code

Running Environment

Ubuntu 16.04 + Python3.7

You can install the environment directly by using conda env create -f env.yml in conda.

Training

We use our 3D caricature dataset and CelebA-Mask-HQ dataset to train 3D-CariGAN. You could download CelebA-Mask-HQ dataset and then reconstruct their 3D normal heads of all images. The 3D normal heads are for calculating loss.

Inferring

The inferring code is cari_pipeline.py file in pipeline folder. You could train your model or use our pre-trained model.

The pipeline includes two optional sub-program eye_complete and color_complete, which are implemented by C++. You should compile them and then use them. The eye_complete is for completing the eye part of mesh and the color_complete is for texture completion.

Pre-trained Model

You can download pre-trained model latest.pth in google drive and BaiduYun (code: 3kz8). You should put it into ./checkpoints.

Additional notes

Please cite the following paper if the dataset and code help your research:

Citation:

@article{ye2021caricature,
 author = {Ye, Zipeng and Xia, Mengfei and Sun, Yanan and Yi, Ran and Yu, Minjing and Zhang, Juyong and Lai, Yu-Kun and Liu, Yong-Jin},
 title = {3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos},
 journal = {IEEE Transactions on Visualization and Computer Graphics},
 year = {2021},
 doi={10.1109/TVCG.2021.3126659},
}

The paper will be published.

unofficial pytorch implementation of RefineGAN

RefineGAN unofficial pytorch implementation of RefineGAN (https://arxiv.org/abs/1709.00753) for CSMRI reconstruction, the official code using tensorpa

xinby17 5 Jul 21, 2022
UnsupervisedR&R: Unsupervised Pointcloud Registration via Differentiable Rendering

UnsupervisedR&R: Unsupervised Pointcloud Registration via Differentiable Rendering This repository holds all the code and data for our recent work on

Mohamed El Banani 118 Dec 06, 2022
Just Go with the Flow: Self-Supervised Scene Flow Estimation

Just Go with the Flow: Self-Supervised Scene Flow Estimation Code release for the paper Just Go with the Flow: Self-Supervised Scene Flow Estimation,

Himangi Mittal 50 Nov 22, 2022
EfficientNetv2 TensorRT int8

EfficientNetv2_TensorRT_int8 EfficientNetv2模型实现来自https://github.com/d-li14/efficientnetv2.pytorch 环境配置 ubuntu:18.04 cuda:11.0 cudnn:8.0 tensorrt:7

34 Apr 24, 2022
Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

66 Dec 15, 2022
Code for "Adversarial attack by dropping information." (ICCV 2021)

AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa

Ranjie Duan 52 Nov 10, 2022
Model of an AI powered sign language interpreter.

TEXT AND SPEECH TO SIGN LANGUAGE. A web application which takes in text or live audio speech recording as input, converts and displays the relevant Si

Mark Gatere 4 Mar 30, 2022
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)

Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022) Please cite "Independent SE(3)-Equivar

Octavian Ganea 154 Jan 02, 2023
Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network This repository is the implementation of ACE-HGNN in PyTorch. Environment pyt

9 Nov 28, 2022
Deep Learning tutorials in jupyter notebooks.

DeepSchool.io Sign up here for Udemy Course on Machine Learning (Use code DEEPSCHOOL-MARCH to get 85% off course). Goals Make Deep Learning easier (mi

Sachin Abeywardana 1.8k Dec 28, 2022
Universal Probability Distributions with Optimal Transport and Convex Optimization

Sylvester normalizing flows for variational inference Pytorch implementation of Sylvester normalizing flows, based on our paper: Sylvester normalizing

Rianne van den Berg 172 Dec 13, 2022
Deep learning with TensorFlow and earth observation data.

Deep Learning with TensorFlow and EO Data Complete file set for Jupyter Book Autor: Development Seed Date: 04 October 2021 ISBN: (to come) Notebook tu

Development Seed 20 Nov 16, 2022
Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021) Official Pytorch implementation of Unbiased Classification

Youngkyu 17 Jan 01, 2023
Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning

LearningToCompare Pytorch Implementation for Paper: Learning to Compare: Relation Network for Few-Shot Learning Howto download mini-imagenet and make

Jackie Loong 246 Dec 19, 2022
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

mani 1.2k Jan 07, 2023
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation (ICCV 2021)

A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation (ICCV 2021) This repository contains the official implemen

81 Dec 14, 2022
A Python library for working with arbitrary-dimension hypercomplex numbers following the Cayley-Dickson construction of algebras.

Hypercomplex A Python library for working with quaternions, octonions, sedenions, and beyond following the Cayley-Dickson construction of hypercomplex

7 Nov 04, 2022
Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》

Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data. This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition

Zhong Yaoyao 68 Jan 07, 2023
Uni-Fold: Training your own deep protein-folding models.

Uni-Fold: Training your own deep protein-folding models. This package provides and implementation of a trainable, Transformer-based deep protein foldi

DeepModeling 88 Jan 03, 2023