GitHub repository for "Improving Video Generation for Multi-functional Applications"

Overview

Improving Video Generation for Multi-functional Applications

GitHub repository for "Improving Video Generation for Multi-functional Applications"

Paper Link

For more information please refer to our homepage.

Requirements

  • Tensorflow 1.2.1
  • Python 2.7
  • ffmpeg

Data Format

Videos are stored as JPEGs of vertically stacked frames. Every frame needs to be at least 64x64 pixels; videos contain between 16 and 32 frames. For an example datasets see: http://carlvondrick.com/tinyvideo/#data

Training

python main_train.py

Important Parameters:

  • mode: one of 'generate', 'predict', 'bw2rgb', 'inpaint' depending on weather you want to generate videos, predict future frames, colorize videos or do inpainting.
  • batch_size: Recommended 64, for colorization use 32 for memory issues.
  • root_dir: root directory of dataset
  • index_file: must be in root_dir, containing a list of all training data clips; path relative to root_dir.
  • experiment_name: name of experiment
  • output_every: output loss to stdout and write to tensorboard summary every xx steps.
  • sample_every: generate a visual sample every xx steps.
  • save_model_very: save the model every xx steps.
  • recover_model: if true recover model and continue training
Owner
Bernhard Kratzwald
currently: Ph.D. student @ ETH Zurich. past: CS Master @ ETHZ, Bachelor @ TU Vienna
Bernhard Kratzwald
Pytorch tutorials for Neural Style transfert

PyTorch Tutorials This tutorial is no longer maintained. Please use the official version: https://pytorch.org/tutorials/advanced/neural_style_tutorial

Alexis David Jacq 135 Jun 26, 2022
Self-supervised learning on Graph Representation Learning (node-level task)

graph_SSL Self-supervised learning on Graph Representation Learning (node-level task) How to run the code To run GRACE, sh run_GRACE.sh To run GCA, sh

Namkyeong Lee 3 Dec 31, 2021
Adjust Decision Boundary for Class Imbalanced Learning

Adjusting Decision Boundary for Class Imbalanced Learning This repository is the official PyTorch implementation of WVN-RS, introduced in Adjusting De

Peyton Byungju Kim 16 Jan 04, 2023
Code for the Paper: Conditional Variational Capsule Network for Open Set Recognition

Conditional Variational Capsule Network for Open Set Recognition This repository hosts the official code related to "Conditional Variational Capsule N

Guglielmo Camporese 35 Nov 21, 2022
Least Square Calibration for Peer Reviews

Least Square Calibration for Peer Reviews Requirements gurobipy - for solving convex programs GPy - for Bayesian baseline numpy pandas To generate p

Sigma <a href=[email protected]"> 1 Nov 01, 2021
The author's officially unofficial PyTorch BigGAN implementation.

BigGAN-PyTorch The author's officially unofficial PyTorch BigGAN implementation. This repo contains code for 4-8 GPU training of BigGANs from Large Sc

Andy Brock 2.6k Jan 02, 2023
Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CCT)

Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CCT) Paper, Project Page This repo contains the official implementation of CVPR

Yassine 344 Dec 29, 2022
Official Pytorch implementation for video neural representation (NeRV)

NeRV: Neural Representations for Videos (NeurIPS 2021) Project Page | Paper | UVG Data Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav S

hao 214 Dec 28, 2022
Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

BERT Got a Date: Introducing Transformers to Temporal Tagging Satya Almasian*, Dennis Aumiller*, and Michael Gertz Heidelberg University Contact us vi

54 Dec 04, 2022
Use AI to generate a optimized stock portfolio

Use AI, Modern Portfolio Theory, and Monte Carlo simulation's to generate a optimized stock portfolio that minimizes risk while maximizing returns. Ho

Greg James 30 Dec 22, 2022
I-BERT: Integer-only BERT Quantization

I-BERT: Integer-only BERT Quantization HuggingFace Implementation I-BERT is also available in the master branch of HuggingFace! Visit the following li

Sehoon Kim 139 Dec 27, 2022
This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".

ResT By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software Technology at Nanjing University] This repo is the official implement

zhql 222 Dec 13, 2022
A repository for generating stylized talking 3D and 3D face

style_avatar A repository for generating stylized talking 3D faces and 2D videos. This is the repository for paper Imitating Arbitrary Talking Style f

Haozhe Wu 191 Dec 22, 2022
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

Yihui He 1k Jan 03, 2023
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning This repository is the official implementation of CARE.

ChongjianGE 89 Dec 02, 2022
My usage of Real-ESRGAN to upscale anime, some test and results in the test_img folder

anime upscaler My usage of Real-ESRGAN to upscale anime, I hope to use this on a proper GPU cuz doing this on CPU is completely shit 😂 , I even tried

Shangar Muhunthan 29 Jan 07, 2023
EXplainable Artificial Intelligence (XAI)

EXplainable Artificial Intelligence (XAI) This repository includes the codes for different projects on eXplainable Artificial Intelligence (XAI) by th

4 Nov 28, 2022
SSL_SLAM2: Lightweight 3-D Localization and Mapping for Solid-State LiDAR (mapping and localization separated) ICRA 2021

SSL_SLAM2 Lightweight 3-D Localization and Mapping for Solid-State LiDAR (Intel Realsense L515 as an example) This repo is an extension work of SSL_SL

Wang Han 王晗 1.3k Jan 08, 2023
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

ChongjianGE 89 Dec 02, 2022
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.

Ubiquitous Knowledge Processing Lab 22 Jan 02, 2023