  Existing human datasets for avatar creation are typically limited to
laboratory environments, wherein high-quality annotations (e.g., SMPL
estimation from 3D scans or multi-view images) can be ideally provided.
However, their annotating requirements are impractical for real-world images or
videos, posing challenges toward real-world applications on current avatar
creation methods. To this end, we propose the WildAvatar dataset, a web-scale
in-the-wild human avatar creation dataset extracted from YouTube, with
$10,000+$ different human subjects and scenes. WildAvatar is at least
$10\times$ richer than previous datasets for 3D human avatar creation. We
evaluate several state-of-the-art avatar creation methods on our dataset,
highlighting the unexplored challenges in real-world applications on avatar
creation. We also demonstrate the potential for generalizability of avatar
creation methods, when provided with data at scale. We publicly release our
data source links and annotations, to push forward 3D human avatar creation and
other related fields for real-world applications.
