Such simulations are typically carried out by physics engines that require manual human expertise and are subjectto. The proposed model is adaptable to a large variety of garments and changing topologies, without need of being retrained. We introduce PhysXNet, a learning-based approach to predict the dynamics of deformable clothes given 3D skeleton motion sequences of humans wearing these clothes. Extensive experiments have been conducted on the proposed dataset to verify its significance and usefulness. A novel adaptable template is proposed to enable the learning of all types of clothing in a single network. To demonstrate the advantage of Deep Fashion3D, we propose a novel baseline approach for single-view garment reconstruction, which leverages the merits of both mesh and implicit representations. In addition, each garment is randomly posed to enhance the variety of real clothing deformations. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances. We propose to fill this gap by introducing Deep Fashion3D, the largest collection to date of 3D garment models, with the goal of establishing a novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. In contrast, modeling and recovering clothed human and 3D garments remains notoriously difficult, mostly due to the lack of large-scale clothing models available for the research community. SMPL, learned from a large number of body scans. Recent advances in learning-based approaches have accomplished unprecedented accuracy in recovering unclothed human shape and pose from single images, thanks to the availability of powerful statistical models, e.g. High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc.
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