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:
this https URL

介绍了 4D-DRESS，这是第一个以高质量 4D 纹理扫描和服装网格推进人类服装研究的真实世界 4D 数据集，拥有 64 套服装和 520 个人体运动序列的 78k 纹理扫描，通过半自动的 4D 人体解析流程标注和分割详细复杂的 4D 人体扫描，为服装模拟和重构建立了多个基准，为逼真人体服装研究的进步铺平了道路。

4D-DRESS：带语义注释的现实世界人类服装的 4D 数据集

4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic  Annotations

We present a learning algorithm that uses bone-driven motion networks to
predict the deformation of loose-fitting garment meshes at interactive rates.
Given a garment, we generate a simulation database and extract virtual bones
from simulated mesh sequences using skin decomposition. At runtime, we
separately compute low- and high-frequency deformations in a sequential manner.
The low-frequency deformations are predicted by transferring body motions to
virtual bones' motions, and the high-frequency deformations are estimated
leveraging the global information of virtual bones' motions and local
information extracted from low-frequency meshes. In addition, our method can
estimate garment deformations caused by variations of the simulation parameters
(e.g., fabric's bending stiffness) using an RBF kernel ensembling trained
networks for different sets of simulation parameters. Through extensive
comparisons, we show that our method outperforms state-of-the-art methods in
terms of prediction accuracy of mesh deformations by about 20% in RMSE and 10%
in Hausdorff distance and STED. The code and data are available at
this https URL

该研究通过骨骼运动网络来预测宽松服装网格的变形，包括低频和高频的变形，并可估算仿真参数变化引起的衣服变形，证明了该方法优于现有方法。