《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

Overview

Towards High Fidelity Face-Relighting with Realistic Shadows

Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2021.

alt text alt text

The code for this project was developed using Python 3 and Tensorflow 1.9.0.

Trained model

To run our trained model on an input image and a target lighting:

python test_relight_single_image.py input_image_path target_lighting_path output_image_path gpu_id

An example of this is provided below:

python test_relight_single_image.py sample_images/01503.png sample_lightings/light_left.txt sample_outputs/01503_left.png 7

Citation

If you utilize our code in your work, please cite our CVPR 2021 paper.

@inproceedings{ towards-high-fidelity-face-relighting-with-realistic-shadows,
  author = { Andrew Hou and Ze Zhang and Michel Sarkis and Ning Bi and Yiying Tong and Xiaoming Liu },
  title = { Towards High Fidelity Face Relighting with Realistic Shadows },
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = { 2021 }
}

Contact

If there are any questions, please feel free to post here or contact the authors at {houandr1, zhangze6, ytong, liuxm}@msu.edu, {msarkis, nbi}@qti.qualcomm.com

GAN encoders in PyTorch that could match PGGAN, StyleGAN v1/v2, and BigGAN. Code also integrates the implementation of these GANs.

MTV-TSA: Adaptable GAN Encoders for Image Reconstruction via Multi-type Latent Vectors with Two-scale Attentions. This is the official code release fo

owl 37 Dec 24, 2022
Dilated RNNs in pytorch

PyTorch Dilated Recurrent Neural Networks PyTorch implementation of Dilated Recurrent Neural Networks (DilatedRNN). Getting Started Installation: $ pi

Zalando Research 200 Nov 17, 2022
We utilize deep reinforcement learning to obtain favorable trajectories for visual-inertial system calibration.

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning Update: The lastest code will be updated in this branch. Pleas

ETHZ ASL 27 Dec 29, 2022
MMRazor: a model compression toolkit for model slimming and AutoML

Documentation: https://mmrazor.readthedocs.io/ English | 简体中文 Introduction MMRazor is a model compression toolkit for model slimming and AutoML, which

OpenMMLab 899 Jan 02, 2023
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

haifeng xia 32 Oct 26, 2022
🚩🚩🚩

My CTF Challenges 2021 AIS3 Pre-exam / MyFirstCTF Name Category Keywords Difficulty ⒸⓄⓋⒾⒹ-①⑨ (MyFirstCTF Only) Reverse Baby ★ Piano Reverse C#, .NET ★

6 Oct 28, 2021
Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting Note: You can find here the accompanying seq2seq RNN forecas

Guillaume Chevalier 1k Dec 25, 2022
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le

deargen 11 Nov 19, 2022
PushForKiCad - AISLER Push for KiCad EDA

AISLER Push for KiCad Push your layout to AISLER with just one click for instant

AISLER 31 Dec 29, 2022
Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization

FAC-Net Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization Linjiang Huang (CUHK), Liang Wang (CASIA), Hongsheng

21 Nov 22, 2022
CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches This document describes how to install and use CRISCE (CRItical

Chair of Software Engineering II, Uni Passau 2 Feb 09, 2022
Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Det

123 Jan 04, 2023
A TensorFlow implementation of FCN-8s

FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is

Pierluigi Ferrari 50 Aug 08, 2022
Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom

Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom Sample on-line plotting while training(avg loss)/testing(writ

Jingwei Zhang 269 Nov 15, 2022
Dynamic Bottleneck for Robust Self-Supervised Exploration

Dynamic Bottleneck Introduction This is a TensorFlow based implementation for our paper on "Dynamic Bottleneck for Robust Self-Supervised Exploration"

Bai Chenjia 4 Nov 14, 2022
PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017

pytorch-deep-generative-replay PyTorch implementation of Continual Learning with Deep Generative Replay, NIPS 2017 Results Continual Learning on Permu

Junsoo Ha 127 Dec 14, 2022
Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF)

Graph Convolutional Gated Recurrent Neural Network (GCGRNN) Improved from Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF

Lei Lin 21 Dec 18, 2022
Official implementation of FCL-taco2: Fast, Controllable and Lightweight version of Tacotron2 @ ICASSP 2021

FCL-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech synthesis (ICASSP 2021) Paper | Demo Block diagram of FCL-taco2, where the decode

Disong Wang 39 Sep 28, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
A self-supervised 3D representation learning framework named viewpoint bottleneck.

Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In

63 Aug 11, 2022