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Recent advances in large models have significantly advanced image-to-3D reconstruction. However, the generated models are often fused into a single piece, limiting their applicability in downstream tasks. This paper focuses on 3D garment generation, a key area for applications like virtual try-on with dynamic garment animations, which require garments to be separable and simulation-ready. We introduce Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image. Starting with the image, our approach combines a pre-trained image-to-sewing pattern generation model for creating coarse sewing patterns with a pre-trained multi-view diffusion model to produce multi-view images. The sewing pattern is further refined using a differentiable garment simulator based on the generated multi-view images. Versatile experiments demonstrate that our optimization approach substantially enhances the geometric alignment of the reconstructed 3D garments and humans with the input image. Furthermore, by integrating a texture generation module and a human motion generation module, we produce customized physics-plausible and realistic dynamic garment demonstrations. Our project page is https://dress-1-to-3.github.io/. 

Abstract Single-crystalline nickel-rich cathodes are a rising candidate with great potential for high-energy lithium-ion batteries due to their superior structural and chemical robustness in comparison with polycrystalline counterparts. Within the single-crystalline cathode materials, the lattice strain and defects have significant impacts on the intercalation chemistry and, therefore, play a key role in determining the macroscopic electrochemical performance. Guided by our predictive theoretical model, we have systematically evaluated the effectiveness of regaining lost capacity by modulating the lattice deformation via an energy-efficient thermal treatment at different chemical states. We demonstrate that the lattice structure recoverability is highly dependent on both the cathode composition and the state of charge, providing clues to relieving the fatigued cathode crystal for sustainable lithium-ion batteries. 

Abstract Surface lattice reconstruction is commonly observed in nickel-rich layered oxide battery cathode materials, causing unsatisfactory high-voltage cycling performance. However, the interplay of the surface chemistry and the bulk microstructure remains largely unexplored due to the intrinsic structural complexity and the lack of integrated diagnostic tools for a thorough investigation at complementary length scales. Herein, by combining nano-resolution X-ray probes in both soft and hard X-ray regimes, we demonstrate correlative surface chemical mapping and bulk microstructure imaging over a single charged LiNi_0.8Mn_0.1Co_0.1O₂(NMC811) secondary particle. We reveal that the sub-particle regions with more micro cracks are associated with more severe surface degradation. A mechanism of mutual modulation between the surface chemistry and the bulk microstructure is formulated based on our experimental observations and finite element modeling. Such a surface-to-bulk reaction coupling effect is fundamentally important for the design of the next generation battery cathode materials.