license: apache-2.0
Dataset Card for the RealTalk Video Dataset
Thank you for your interest in the RealTalk dataset! RealTalk consists of 692 in-the-wild videos of dyadic (i.e. two person) conversations, curated with the goal of advancing multimodal communication research in computer vision. If you find our dataset useful, please cite
@inproceedings{geng2023affective,
title={Affective Faces for Goal-Driven Dyadic Communication},
author={Geng, Scott and Teotia, Revant and Tendulkar, Purva and Menon, Sachit and Vondrick, Carl},
year={2023}
}
Dataset Details
The dataset contains 692 full-length videos scraped from The Skin Deep, a public YouTube channel that captures long-form, unscripted conversations between diverse indivudals about different facets of the human experience. We also include associated annotations; we detail all files present in the dataset below.
File Overview
General notes:
- All frame numbers are indexed from 0.
- We denote 'p0' as the person on the left side of the video, and 'p1' as the person on the right side.
- denotes the unique 11 digit video ID assigned by YouTube to a specific video.
[0] videos/videos_{xx}.tar
Contains the full-length raw videos that the dataset is created from in shards of 50. Each video is stored at 25 fps in avi format.
Each video is stored with filename <video_id>.avi (e.g., 5hxY5Svr2aM.avi).
[1] audio.tar.gz
Contains audio files extracted from the videos, stored in mp3 format.
[2] asr.tar.gz
Contains ASR outputs of Whisper for each video. Subtitles for video <video_id>.avi are stored in the file <video_id>.json as the dictionary
{
'text': <full asr transcript of video>
'segments': <time-stamped ASR segments>
'language': <detected language of video>
}
[3.0] benchmark/train_test_split.json
This json file describes the clips used as the benchmark train/test split in our paper. The file stores the dictionary
{
'train': [list of train samples],
'test': [list of test samples]
}
where each entry in the list is another dictionary with format
{
'id': [video_id, start_frame (inclusive), end_frame (exclusive)],
'speaker': 'p0'|'p1'
'listener': 'p0'|'p1'
'asr': str
}
The ASR of the clip is computed with Whisper.
[3.1] benchmark/embeddings.pkl
Pickle file containing visual embeddings of the listener frames in the training/testing clips, as computed by several pretrained face models implemented in deepface. The file stores a dictionary with format
{
f'{video_id}.{start_frame}.{end_frame}:{
{
<model_name_1>: <array of listener embeddings>,
<model_name_2>: <array of listener embeddings>,
...
}
...
}
[4] annotations.tar.gz
Contains face bounding box and active speaker annotations for every frame of each video. Annotations for video <video_id>.avi are contained in file <video_id>.json, which stores a nested dictionary structure:
{str(frame_number):{
'people':{
'p0':{'score': float, 'bbox': array}
'p1':{'score': float, 'bbox': array}
}
'current_speaker': 'p0'|'p1'|None
}
...
}
The 'score' field stores the active speaker score as predicted by TalkNet-ASD; larger positive values indicate a higher probability that the person is speaking. Note also that the 'people' subdictionary may or may not contain the keys 'p0', 'p1', depending on who is visible in the frame.
[5] emoca.tar.gz
Contains EMOCA embeddings for almost all frames in all the videos. The embeddings for<video_id>.avi are contained in the pickle file <video_id>.pkl, which has dictionary structure
{
int(frame_number):{
'p0': <embedding dict from EMOCA>,
'p1': <embedding dict from EMOCA>
}
...
}
Note that some frames may be missing embeddings due to occlusions or failures in face detection.
Dataset Card Authors
Scott Geng