Virtual try-on (VTON) aims to synthesize specific fashion images dressed in given garments, which possesses great potential in real-world scenarios. Existing methods generally stand on the shoulder of the single-view VTON to train a warping model and then fit the given garments onto the human body under a fixed posture and viewpoint, which often fails to preserve the consistent garment characteristics in across-view and multi-pose guided try-on scenarios due to the lack of both across-view data and effective view consistency training. To alleviate this dilemma, we propose a fresh view consistency-driven VTON task (VC-VTON) and release a multi-view virtual try-on dataset with complete annotation (e.g., viewpoint, text, posture, parsing maps, etc.) to encourage across-view training scenarios. Based on this hard-won dataset, we further propose VC-TwinNet, a Twin-UNet baseline based on spatiotemporal-aware View Consistency training, designed specifically for the challenging task. Specifically, to enable view-aware denoising and sparse-to-continuous view generalization, we introduce RoPE and circle embedding to represent the relative and continuous position relation across viewpoints, serving to distinguish their outfitting appearance and warping states. Afterwards, to implicitly learn the interactions across views under given multiple posture conditions, we further contribute a spatiotemporal-aware view attention module to capture the spatial and temporal details for across-view training. Moreover, we utilize an across-view consistency loss to supervise the model training, to ultimately improve the performance of our VC-VTON. Extensive experiments demonstrate the superiority of our approach and state-of-the-art results on various evaluations without declining single-view performance. And as for practicality and timeliness, our proposed components are essentially plug-and-play and remain effective in the new DiT-centered paradigm.

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability. Previous methods based on Score Distillation Sampling (SDS) can produce diversified 3D results by distilling 3D knowledge from large 2D diffusion models, but they usually suffer from long per-case optimization time with inconsistent issues. Recent works address the problem and generate better 3D results either by finetuning a multi-view diffusion model or training a fast feed-forward model. However, they still lack intricate textures and complex geometries due to inconsistency and limited generated resolution. To simultaneously achieve high fidelity, consistency, and efficiency in single image-to-3D, we propose a novel framework Unique3D that includes a multi-view diffusion model with a corresponding normal diffusion model to generate multi-view images with their normal maps, a multi-level upscale process to progressively improve the resolution of generated orthographic multi-views, as well as an instant and consistent mesh reconstruction algorithm called ISOMER, which fully integrates the color and geometric priors into mesh results. Extensive experiments demonstrate that our Unique3D significantly outperforms other image-to-3D baselines in terms of geometric and textural details.