  The studies of human clothing for digital avatars have predominantly relied
on synthetic datasets. While easy to collect, synthetic data often fall short
in realism and fail to capture authentic clothing dynamics. Addressing this
gap, we introduce 4D-DRESS, the first real-world 4D dataset advancing human
clothing research with its high-quality 4D textured scans and garment meshes.
4D-DRESS captures 64 outfits in 520 human motion sequences, amounting to 78k
textured scans. Creating a real-world clothing dataset is challenging,
particularly in annotating and segmenting the extensive and complex 4D human
scans. To address this, we develop a semi-automatic 4D human parsing pipeline.
We efficiently combine a human-in-the-loop process with automation to
accurately label 4D scans in diverse garments and body movements. Leveraging
precise annotations and high-quality garment meshes, we establish several
benchmarks for clothing simulation and reconstruction. 4D-DRESS offers
realistic and challenging data that complements synthetic sources, paving the
way for advancements in research of lifelike human clothing. Website:
https://ait.ethz.ch/4d-dress.
