Garment representation, editing and animation are challenging topics in the area of computer vision and graphics. It remains difficult for existing garment representations to achieve smooth and plausible transitions between different shapes and topologies. In this work, we introduce, DeepCloth, a unified framework for garment representation, reconstruction, animation and editing. Our unified framework contains 3 components: First, we represent the garment geometry with a "topology-aware UV-position map", which allows for the unified description of various garments with different shapes and topologies by introducing an additional topology-aware UV-mask for the UV-position map. Second, to further enable garment reconstruction and editing, we contribute a method to embed the UV-based representations into a continuous feature space, which enables garment shape reconstruction and editing by optimization and control in the latent space, respectively. Finally, we propose a garment animation method by unifying our neural garment representation with body shape and pose, which achieves plausible garment animation results leveraging the dynamic information encoded by our shape and style representation, even under drastic garment editing operations. To conclude, with DeepCloth, we move a step forward in establishing a more flexible and general 3D garment digitization framework. Experiments demonstrate that our method can achieve state-of-the-art garment representation performance compared with previous methods.
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket…
ChatGarment: Garment Estimation, Generation and Editing via Large Language Models
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Shanghai Jiao Tong University（上海交通大学）；EPFL（洛桑联邦理工学院）；Westlake University（西湖大学）；ETH Zürich（苏黎世联邦理工学院）；Mila, University of Montreal（蒙特利尔大学米拉研究所）；Meshcapade（Meshcapade公司）；Stanford University（斯坦福大学）
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Carnegie Mellon University（卡内基梅隆大学）；UiT The Arctic University of Norway（挪威北极大学）；ByteDance（字节跳动）
Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing. To this end, we propose a characteristic-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive GAN++ (PASTA-GAN++), to achieve a versatile system for high-resolution unpaired virtual try-…
The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often hinder their ability to innovate during the design thinking and ideation process. In the fashion industry, in particular, the growing diversity of customers' needs, the intense global competition, and the shrinking time-to-market (a.k.a., "fast fashion") further exacerbate this challenge for designers. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation and/or editing of design c…
DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image Editing
Shenzhen Technology University（深圳科技大学）；University of Washington（华盛顿大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）
3D model reconstruction from a single image has achieved great progress with the recent deep generative models. However, the conventional reconstruction approaches with template mesh deformation and implicit fields have difficulty in reconstructing non-watertight 3D mesh models, such as garments. In contrast to image-based modeling, the sketch-based approach can help users generate 3D models to meet the design intentions from hand-drawn sketches. In this study, we propose Sketch2Cloth, a sketch-based 3D garment generation system using the unsigned distance fields from the user's sketch input. …
Neural Clothing Tryer: Customized Virtual Try-On via Semantic Enhancement and Controlling Diffusion Model
Guangdong University of Technology, Guangzhou 510006, China；Guangdong Provincial Key Laboratory of Intellectual Property \& Big Data, Guangzhou 510006, China；South China University of Technology, Guangzhou 510006, China；Genstoraige Technology (Beijing) Co., Ltd, Beijing 100000, China
Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns
University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Meta
Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates
CVLab, École Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院（EPFL）计算机视觉实验室）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；Pinscreen and Mohamed bin Zayed University of Artificial Intelligence（Pinscreen和马尔代夫人工智能大学）
Adobe Research（Adobe研究院）；University College London（伦敦大学学院）
Synthesizing dynamic appearances of humans in motion plays a central role in applications such as AR/VR and video editing. While many recent methods have been proposed to tackle this problem, handling loose garments with complex textures and high dynamic motion still remains challenging. In this paper, we propose a video based appearance synthesis method that tackles such challenges and demonstrates high quality results for in-the-wild videos that have not been shown before. Specifically, we adopt a StyleGAN based architecture to the task of person specific video based motion retargeting. We i…
