Design2Cloth: 3D Cloth Generation from 2D Masks
This work addresses the need for automatic, high-fidelity garment design in the fashion industry, offering a user-friendly tool for creating diverse and detailed clothes from simple 2D inputs.
The paper tackles the problem of generating realistic 3D clothes from 2D masks, proposing Design2Cloth, which outperforms state-of-the-art models by a large margin, as demonstrated in qualitative and quantitative experiments.
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.