To enable large-scale reuse of real-world 3D assets-where garments and characters rarely share skeletons, templates, or dense correspondences-we present a fully automated virtual try-on system that dresses complex, multi-layer garments onto diverse, arbitrarily posed humanoids. Our key idea is to use SMPL as an intermediate proxy and decompose clothing-to-body transfer into two correspondence tasks with distinct challenges: (1) clothing-to-SMPL (partial-to-complete alignment) and (2) body-to-SMPL (large pose/shape variation and stylization). We address clothing-to-SMPL using a geometry-driven correspondence model, and introduce a diffusion-based body-to-SMPL correspondence approach that leverages multi-view consistent appearance features together with a pretrained 2D foundation model. Using these correspondences, we register SMPL/SMPL+D (Displacement) to the garment and target body and then perform simulator-driven fitting by transferring the garment along a smooth SMPL→SMPL+D transition, producing physically plausible draping on the target. Our system handles complex garment topology (including non-manifold meshes) and generalizes to a wide range of humanoid characters (e.g., humans, robots, cartoons, and creatures) while remaining computationally practical. Upon draping, our system also supports fast customization of clothing size. We show that our system can produce high-quality 3D clothing fittings without any human labor, even when 2D clothing sewing patterns are not available. Our project page is: https://cao-cong0.github.io/LUIVITON-Learned-Universal-Interoperable-VIrtual-Try-ON/.

Microwave synthetic aperture interferometric radiometers (SAIRs) are very powerful instruments for high-resolution remote sensing of the atmosphere and the earth surfaces at microwave frequencies. Microwave SAIR imaging reconstruction from interferometric measurements suffers from hardware non-identities, limited prior information, and noise interference, and consequently often requires expert calibration strategies to reduce imaging error and improve the accuracy of the reconstruction. In this article, we propose a new SAIR imaging approach with a deep convolutional neural network (CNN) learning framework to optimize the reconstruction performance. We interpret interferometric measurements of SAIR as a signal encoding representation and SAIR imaging as the corresponding decoding representation. A deep CNN framework with additional fully connected layers is utilized to autonomously learn the decoding representation from interferometric measurement samples and perform SAIR imaging. The supervised learning forward model with hyperparameters makes that the proposed approach could accurately obtain the SAIR imaging representation involving multiple systematic features for real applications. We demonstrate the performance of the proposed imaging approach through extensive numerical experiments. Compared with conventional handcrafted Fourier transform and sparse regularization reconstruction imaging approaches, the proposed imaging approach based on deep learning is superior in terms of image quality, computing efficiency, and noise suppression.