Description AVAPrintDB is the first fully public multi-generator avatar fingerprinting resource, published in the paper "Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark", and combines: A large-scale photorealistic talking-head avatar database, Atandardized evaluation protocols, Publicly available benchmark code, Pretrained checkpoints, Preprocessing pipelines, and reproducibility resources. The benchmark spans 66k+ avatar videos generated with three state-of-the-art avatar generators from distinct synthesis paradigms (GAGAvatar (Neurips 2024), LivePortrait (2025), and HunyuanPortrait (CVPR 2025)), two audiovisual source datasets (RAVDESS and CREMA-D), and multiple evaluation settings including generator shift, dataset shift, and demographic analysis. Contents Here we provide the database files used for the benchmark, containing: MP4 files of photorealistic avatar videos, in videos.zip Preprocessed landmarks for those videos, in landmarks.zip Preprocessed embeddings from CLIP and DiNOv2, in embeddings.zip Metadata for database expansion and reproducibility, in metadata.zip For more details about the database generation process, the preprocessing steps and the associated benchmark code, please check the AVAPrintDB GitHub page. File naming convention For all the MP4 video files, preprocessed landmark files (*.npy) and preprocessed embeddings (*.pt), we follow the same naming convention: ------. Example: Actor_09--Actor_16--aeca9daf-bdfd-553f-8287-92b94732bd61--GAGA.npy Corresponds to a landmarks file with extension "npy", target_id = "Actor_09", driving_id = "Actor_16", UUID of driving video = "aeca9daf-bdfd-553f-8287-92b94732bd61", and generator used = "GAGA". The mapping between UUIDs and the original driving video can be found in the metadata file avaprintdb_metadata.csv. License AVAPrintDB is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0 Citation @article{pedrouzo2026leveraging, title={Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark}, author={Pedrouzo-Rodriguez, Laura and Gomez, Luis F and Tolosana, Ruben and Vera-Rodriguez, Ruben and Daza, Roberto and Morales, Aythami and Fierrez, Julian}, journal={arXiv preprint arXiv:2603.26934}, year={2026} }