WildAvatar: Learning In-the-wild 3D Avatars from the Web

Existing research on avatar creation is typically limited to laboratory datasets, which require high costs against scalability and exhibit insufficient representation of the real world. On the other hand, the web abounds with off-the-shelf real-world human videos, but these videos vary in quality and require accurate annotations for avatar creation. To this end, we propose an automatic annotating pipeline with filtering protocols to curate these humans from the web. Our pipeline surpasses state-of-the-art methods on the EMDB benchmark, and the filtering protocols boost verification metrics on web videos. We then curate WildAvatar, a web-scale in-the-wild human avatar creation dataset extracted from YouTube, with $10000+$ different human subjects and scenes. WildAvatar is at least $10\times$ richer than previous datasets for 3D human avatar creation and closer to the real world. To explore its potential, we demonstrate the quality and generalizability of avatar creation methods on WildAvatar. We will publicly release our code, data source links and annotations to push forward 3D human avatar creation and other related fields for real-world applications.

@article{huang2025_2407.02165,
  title={ WildAvatar: Learning In-the-wild 3D Avatars from the Web },
  author={ Zihao Huang and Shoukang Hu and Guangcong Wang and Tianqi Liu and Yuhang Zang and Zhiguo Cao and Wei Li and Ziwei Liu },
  journal={arXiv preprint arXiv:2407.02165},
  year={ 2025 }
}