Projects sharing features with MUNIT

We have a reimplementation of the UNIT method that is more performant. It is avaiable at Imaginaire

StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

This code aims to reproduce results obtained in the paper "Visual Feature Attribution using Wasserstein GANs" (official repo, TensorFlow code)

pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework pro

This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove

Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation. It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses. The core of video-to-video…

This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and

This repository contains the TensorFlow code for our NeurIPS 2018 paper “Unsupervised Attention-guided Image-to-Image Translation”. This code is based on the TensorFlow implementation of CycleGAN provided by Harry Yang. You may need to train several times as the quality of the results are…

CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns

Official implementation of GANimation. In this work we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describe in a continuous manifold the anatomical facial movements defining a human expression. Our approach permits controlling the magnitude of…

Project page for End-to-end Recovery of Human Shape and Pose

Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik

Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

The author's officially unofficial PyTorch BigGAN implementation.

Attention-based Deep Multiple Instance Learning

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)

3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 paper, 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation.

CVPR'18 ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)