CLOTH4D: A Dataset for Clothed Human Reconstruction

Xingxing Zou, Xintong Han, Waikeung Wong; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 12847-12857


Clothed human reconstruction is the cornerstone for creating the virtual world. To a great extent, the quality of recovered avatars decides whether the Metaverse is a passing fad. In this work, we introduce CLOTH4D, a clothed human dataset containing 1,000 subjects with varied appearances, 1,000 3D outfits, and over 100,000 clothed meshes with paired unclothed humans, to fill the gap in large-scale and high-quality 4D clothing data. It enjoys appealing characteristics: 1) Accurate and detailed clothing textured meshes---all clothing items are manually created and then simulated in professional software, strictly following the general standard in fashion design. 2) Separated textured clothing and under-clothing body meshes, closer to the physical world than single-layer raw scans. 3) Clothed human motion sequences simulated given a set of 289 actions, covering fundamental and complicated dynamics. Upon CLOTH4D, we novelly designed a series of temporally-aware metrics to evaluate the temporal stability of the generated 3D human meshes, which has been overlooked previously. Moreover, by assessing and retraining current state-of-the-art clothed human reconstruction methods, we reveal insights, present improved performance, and propose potential future research directions, confirming our dataset's advancement. The dataset is available at

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@InProceedings{Zou_2023_CVPR, author = {Zou, Xingxing and Han, Xintong and Wong, Waikeung}, title = {CLOTH4D: A Dataset for Clothed Human Reconstruction}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {12847-12857} }