{'fraction_non_alphanumeric': 0.031228313671061762, 'fraction_numerical': 0.008327550312283136, 'mean_word_length': 5.110169491525424, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
DeepDraper: Fast and Accurate 3D Garment Draping over a 3D Human Body

Draping a 3D human mesh has garnered broad interest due to its wide applicability in virtual try-on, animations, etc. The 3D garment deformations produced by the existing methods are often inconsistent with the body shape, pose, and measurements. This paper proposes a single unified learning-based framework (DeepDraper) to predict garment deformation as a function of body shape, pose, measurements, and garment styles. We train the DeepDraper with coupled geometric and multi-view perceptual losses. Unlike existing methods, we additionally model garment deformations as a function of standard body measurements, which generally a buyer or a designer uses to buy or design perfect fit clothes. As a result, DeepDraper significantly outperforms the state-of-the-art deep network-based approaches in terms of fitness and realism and generalizes well to the unseen style of the garments. In addition to that, DeepDraper is ~ 10 times smaller in size and ~ 23 times faster than the closest state-of-the-art method (TailorNet), which favors its use in real-time applications with less computational power. Despite being trained on the static poses of the TailorNet [32] dataset, DeepDraper generalizes well to unseen body shapes, poses, and garment styles and produces temporally coherent garment deformations on the pose sequences even from the unseen AMASS [25] dataset.