README.md

metadata

pretty_name: RareFace-50
license: cc-by-nc-4.0
tags:
  - faces
  - personalization
  - avatars
  - talking-head
  - youtube

RareFace-50 (from Low-Rank Head Avatar Personalization with Registers)

Dataset for Low-Rank Head Avatar Personalization with Registers. Also available on arxiv.

Project Page

Dataset Summary

RareFace-50 is a curated collection of challenging human faces intended for evaluating personalization of talking-head and avatar generation methods.

Unlike many existing face video datasets that focus primarily on celebrities and well-known public figures (e.g., television personalities), RareFace-50 deliberately targets underrepresented facial appearances, with an emphasis on:

• Distinctive facial details (e.g., pronounced wrinkles, unique tattoos, scars, or other high-frequency details),
• Wide variation in age and appearance,
• High-resolution, close-up footage.

The dataset is constructed from 50 identities, each with 2 short clips, for a total of 100 clips. Source videos are high-resolution interview-style recordings (1080p, 2K, and 4K) collected from YouTube public broadcasts. The average duration of each clip is around 15 seconds.

Important:
This repository contains only metadata about the clips (YouTube links and temporal trim information) in a CSV file.

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Dataset Structure

Files

The dataset is provided as a single CSV file in this repository (RareFace50.csv).

Each row corresponds to a two clip and includes:

• A YouTube link for the source video.
• Two start times and end times defining the clips within that video.

Timestamp format: All temporal fields are stored as strings in h:mm:ss format
(e.g., 0:00:13, 0:01:05, 1:23:45).

Schema

• youtube_url (string)
  Full YouTube URL for the source video.

• start_time (string)
  Clip start time in h:mm:ss.

• end_time (string)
  Clip end time in h:mm:ss.

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How to Use

Loading the CSV

You can access the CSV directly using Python’s standard tools or datasets:

from datasets import load_dataset

ds = load_dataset("StonyBrook-CVLab/RareFace-50")
print(ds["train"][0])

If you use this dataset, please be so kind to cite us:

@inproceedings{
chakkera2025lowrank,
title={Low-Rank Head Avatar Personalization with Registers},
author={Sai Tanmay Reddy Chakkera and Aggelina Chatziagapi and Md Moniruzzaman and Chen-ping Yu and Yi-Hsuan Tsai and Dimitris Samaras},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={(https://openreview.net/pdf?id=mhARf5VzCn)}
}