TalkingHead-1KH is a talking-head dataset consisting of YouTube videos

Overview

TalkingHead-1KH Dataset

Python 3.7 License CC Format MP4 Resolution 512×512 Videos 500k

TalkingHead-1KH is a talking-head dataset consisting of YouTube videos, originally created as a benchmark for face-vid2vid:

One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
Ting-Chun Wang (NVIDIA), Arun Mallya (NVIDIA), Ming-Yu Liu (NVIDIA)
https://nvlabs.github.io/face-vid2vid/
https://arxiv.org/abs/2011.15126.pdf

The dataset consists of 500k video clips, of which about 80k are greater than 512x512 resolution. Only videos under permissive licenses are included. Note that the number of videos differ from that in the original paper because a more robust preprocessing script was used to split the videos. For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing.

Download

Unzip the video metadata

First, unzip the metadata and put it under the root directory:

unzip data_list.zip

Unit test

This step downloads a small subset of the dataset to verify the scripts are working on your computer. You can also skip this step if you want to directly download the entire dataset.

bash videos_download_and_crop.sh small

The processed clips should appear in small/cropped_clips.

Download the entire dataset

Please run

bash videos_download_and_crop.sh train

The script will automatically download the YouTube videos, split them into short clips, and then crop and trim them to include only the face regions. The final processed clips should appear in train/cropped_clips.

Evaluation

To download the evaluation set which consists of only 1080p videos, please run

bash videos_download_and_crop.sh val

The processed clips should appear in val/cropped_clips.

We also provide the reconstruction results synthesized by our model here. For each video, we use only the first frame to reconstruct all the following frames.

Furthermore, for models trained using the VoxCeleb2 dataset, we also provide comparisons using another model trained on the VoxCeleb2 dataset. Please find the reconstruction results here.

Licenses

The individual videos were published in YouTube by their respective authors under Creative Commons BY 3.0 license. The metadata file, the download script file, the processing script file, and the documentation file are made available under MIT license. You can use, redistribute, and adapt it, as long as you (a) give appropriate credit by citing our paper, (b) indicate any changes that you've made, and (c) distribute any derivative works under the same license.

Privacy

When collecting the data, we were careful to only include videos that – to the best of our knowledge – were intended for free use and redistribution by their respective authors. That said, we are committed to protecting the privacy of individuals who do not wish their videos to be included.

If you would like to remove your video from the dataset, you can either

  1. Go to YouTube and change the license of your video, or remove your video entirely.
  2. Contact [email protected]. Please include your YouTube video link in the email.

Acknowledgements

This webpage borrows heavily from the FFHQ-dataset page.

Citation

If you use this dataset for your work, please cite

@inproceedings{wang2021facevid2vid,
  title={One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing},
  author={Ting-Chun Wang and Arun Mallya and Ming-Yu Liu},
  booktitle={CVPR},
  year={2021}
}
A project studying the influence of communication in multi-objective normal-form games

Communication in Multi-Objective Normal-Form Games This repo consists of five different types of agents that we have used in our study of communicatio

Willem Röpke 0 Dec 17, 2021
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer

Multimedia Research 484 Dec 29, 2022
S2s2net - Sentinel-2 Super-Resolution Segmentation Network

S2S2Net Sentinel-2 Super-Resolution Segmentation Network Getting started Install

Wei Ji 10 Nov 10, 2022
scikit-learn inspired API for CRFsuite

sklearn-crfsuite sklearn-crfsuite is a thin CRFsuite (python-crfsuite) wrapper which provides interface simlar to scikit-learn. sklearn_crfsuite.CRF i

417 Dec 20, 2022
External Attention Network

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks paper : https://arxiv.org/abs/2105.02358 Jittor code will come soon

MenghaoGuo 357 Dec 11, 2022
Transformer based SAR image despeckling

Transformer based SAR image despeckling Using the code: The code is stable while using Python 3.6.13, CUDA =10.1 Clone this repository: git clone htt

27 Nov 13, 2022
Code and description for my BSc Project, September 2021

BSc-Project Disclaimer: This repo consists of only the additional python scripts necessary to run the agent. To run the project on your own personal d

Matin Tavakoli 20 Jul 19, 2022
Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization'

pytorch-AdaIN This is an unofficial pytorch implementation of a paper, Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization [Hua

Naoto Inoue 873 Jan 06, 2023
Self-supervised Label Augmentation via Input Transformations (ICML 2020)

Self-supervised Label Augmentation via Input Transformations Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin (KAIST) Accepted to ICML 2020 Install de

hankook 96 Dec 29, 2022
TRACER: Extreme Attention Guided Salient Object Tracing Network implementation in PyTorch

TRACER: Extreme Attention Guided Salient Object Tracing Network This paper was accepted at AAAI 2022 SA poster session. Datasets All datasets are avai

Karel 118 Dec 29, 2022
D2LV: A Data-Driven and Local-Verification Approach for Image Copy Detection

Facebook AI Image Similarity Challenge: Matching Track —— Team: imgFp This is the source code of our 3rd place solution to matching track of Image Sim

16 Dec 25, 2022
Informal Persian Universal Dependency Treebank

Informal Persian Universal Dependency Treebank (iPerUDT) Informal Persian Universal Dependency Treebank, consisting of 3000 sentences and 54,904 token

Roya Kabiri 0 Jan 05, 2022
Wind Speed Prediction using LSTMs in PyTorch

Implementation of Deep-Forecast using PyTorch Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting Adapted from original implementation Setu

Onur Kaplan 151 Dec 14, 2022
A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation

##A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation. #USAGE To run the trained classifier on some images: python w

Alex Seewald 13 Nov 17, 2022
Walk with fastai

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Walk with fastai What is this p

Walk with fastai 124 Dec 10, 2022
Blind visual quality assessment on 360° Video based on progressive learning

Blind visual quality assessment on omnidirectional or 360 video (ProVQA) Blind VQA for 360° Video via Progressively Learning from Pixels, Frames and V

5 Jan 06, 2023
Contrastive Fact Verification

VitaminC This repository contains the dataset and models for the NAACL 2021 paper: Get Your Vitamin C! Robust Fact Verification with Contrastive Evide

47 Dec 19, 2022
Efficient Lottery Ticket Finding: Less Data is More

The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical subnetworks) for dense networks, that can be trained in isolation from random initialization to match

VITA 20 Sep 04, 2022
TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1).

M1-tensorflow-benchmark TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1). I was initially testing if Tens

particle 2 Jan 05, 2022
A library for answering questions using data you cannot see

A library for computing on data you do not own and cannot see PySyft is a Python library for secure and private Deep Learning. PySyft decouples privat

OpenMined 8.5k Jan 02, 2023