ShaojunBian's Stars
junyanz/pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch
pytorch/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
AliaksandrSiarohin/first-order-model
This repository contains the source code for the paper First Order Motion Model for Image Animation
extreme-assistant/CVPR2024-Paper-Code-Interpretation
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集,极市团队整理
nashory/gans-awesome-applications
Curated list of awesome GAN applications and demo
MorvanZhou/Tensorflow-Tutorial
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
NVlabs/imaginaire
NVIDIA's Deep Imagination Team's PyTorch Library
yfeng95/DECA
DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)
NVlabs/FUNIT
Translate images to unseen domains in the test time with few example images.
datitran/face2face-demo
pix2pix demo that learns from facial landmarks and translates this into a face
ZeweiChu/PyTorch-Course
JULYEDU PyTorch Course
tencent-ailab/hifi3dface
Code and data for our paper "High-Fidelity 3D Digital Human Creation from RGB-D Selfies".
clpeng/Awesome-Face-Forgery-Generation-and-Detection
A curated list of articles and codes related to face forgery generation and detection.
ICT-VGL/ICT-FaceKit
ICT's Vision and Graphics Lab's morphable face model and toolkit
huawei-noah/Pretrained-IPT
aayushbansal/Recycle-GAN
Unsupervised Video Retargeting (e.g. face to face, flower to flower, clouds and winds, sunrise and sunset)
wywu/ReenactGAN
[ECCV 2018] ReenactGAN: Learning to Reenact Faces via Boundary Transfer
Blade6570/icface
ICface: Interpretable and Controllable Face Reenactment Using GANs
facebookresearch/VCMeshConv
Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.
nabeel3133/combining3Dmorphablemodels
Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]
jpl917/BlendshapeGeneration
a method for generating facial blendshape rigs from a set of example poses of a CG character
diegothomas/Avatar-generation-3DRW2019-
This is the source code of our 3DRW 2019 paper
letmaik/avatar-animator
🤖 Real-time 2D vector-based avatar animator for Zoom and other video-conferencing apps 🎥
nchinaev/MobileFace
znxlwm/FUNIT-pytorch
Pytorch implementation of "Few-Shot Unsupervised Image-to-Image Translation" (ICCV 2019)
cvlab-stonybrook/EmotionNet_CVPR2020
LansburyCH/eos-expression-aware-proxy
Expression-Aware Proxy Generation part of the paper Photo-Realistic Facial Details Synthesis From Single Image
dafuny/mvfnet
Pytorch code for paper: MVF-Net: Multi-View 3D Face Morphable Model Regression
dafuny/Deep3DFaceReconstruction
Deep3DFaceReconstruction
dafuny/RingNet
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision