In recent years, there has been a significant shift in the field of digital
avatar research, towards modeling, animating and reconstructing clothed human
representations, as a key step towards creating realistic avatars. However,
current 3D cloth generation methods are garment specific or trained completely
on synthetic data, hence lacking fine details and realism. In this work, we
make a step towards automatic realistic garment design and propose
Design2Cloth, a high fidelity 3D generative model trained on a real world
dataset from more than 2000 subject scans. To provide vital contribution to the
fashion industry, we developed a user-friendly adversarial model capable of
generating diverse and detailed clothes simply by drawing a 2D cloth mask.
Under a series of both qualitative and quantitative experiments, we showcase
that Design2Cloth outperforms current state-of-the-art cloth generative models
by a large margin. In addition to the generative properties of our network, we
showcase that the proposed method can be used to achieve high quality
reconstructions from single in-the-wild images and 3D scans. Dataset, code and
pre-trained model will become publicly available.

本研究针对数字化人物研究领域的衣物建模、动画和重建方面的最新发展进行了探讨，提出基于真实世界数据集训练的高保真度 3D 生成模型 Design2Cloth，为时尚行业做出了重要贡献，并展示了该方法在从 2D 布料遮罩生成多样化、详细化服装以及从野外图像和 3D 扫描中实现高质量重建方面的优势。