@article{Wu2026, 
author = {Yuhan Wu and Yixuan Zhang and Qing Chang and Junran Peng and Man Zhang and Guang Chen},
title = {DIVA-3D: a diverse 3D talking head dataset from in-the-wild videos},
year = {2026},
journal = {Visual Intelligence},
volume = {4},
pages = {17},
keywords = {Autoregressive model, 3D talking head generation, Audio-visual dataset, Facial animation},
url = {https://www.sciopen.com/article/10.1007/s44267-026-00120-6},
doi = {10.1007/s44267-026-00120-6},
abstract = {The synthesis of lifelike three-dimensional (3D) talking heads from audio requires precise lip synchronization and nuanced facial expressions. However, current methods often fall short of this goal, largely due to the scarcity of large-scale, diverse training data. To address this issue, this paper first presents a novel, semi-automated pipeline to efficiently harvest audio and corresponding 3D facial FLAME data from public videos. We then use this pipeline to construct DIVA-3D, a large-scale, diverse, in-the-wild audio-visual dataset, which contains 73 hours of both Chinese and English data. This is, to our best knowledge, the most topically diverse 3D talking head dataset available, with six distinct domains. Based on DIVA-3D, we propose a robust generative framework that produces highly accurate lip synchronization and natural facial expressions. Finally, we conduct a comprehensive benchmark of state-of-the-art methods using our new dataset. Extensive results validate the effectiveness of our dataset and demonstrate the superior performance of our framework, underscoring its significant practical value of our framework for real-world applications.}
}