Jingyuan Liu

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/.

Existing image-based virtual try-on methods are limited to frontal views and lack real-time performance. While per-garment virtual try-on methods have tackled these issues by adopting per-garment training, they still encounter practical limitations: (1) the robotic mannequin used for per-garment datasets collection is prohibitively expensive; (2) the synthesized garments often misalign with the human body. To address these challenges, we propose a low-barrier approach to collect per-garment datasets using real human bodies, eliminating the need for an expensive robotic mannequin and reducing data collection time from 2 hours to 2 minutes. Additionally, we introduce a hybrid person representation that ensures precise human-garment alignment. We conducted qualitative and quantitative comparisons with state-of-the-art image-based virtual try-on methods to demonstrate the superiority of our method regarding image quality and temporal consistency. Furthermore, most participants in our user study found the system effective in supporting garment purchasing decisions.

The popularity of virtual try-on methods has increased in recent years as they allow users to preview the appearance of garments on themselves without physically wearing them. However, existing image-based methods for general virtual try-on provide limited support to synthesize realistic and consistent garment images under different poses, due to two main difficulties: 1) the dataset used to train these methods contains a vast collection of garments, but they lack fine details of each garment; 2) they synthesize results by warping the front-view image of the target garment in a rest pose, which results in poor quality and detail for other viewpoints and poses. To overcome these drawbacks, per-garment virtual try-on methods train garment-specific networks that can produce high-quality results with fine-grained details for a particular target garment. However, existing per-garment virtual try-on methods require the use of a physical measurement garment, which limits their applicability. In this paper, we propose a novel per-garment virtual try-on method that leverages a virtual measurement garment, which eliminates the need for the physical measurement garment, to guide the synthesis of high-quality and temporally consistent garment images under various poses. Furthermore, we introduce a gap-filling module that effectively fills the gap between the synthesized garment and body parts. We conduct qualitative and quantitative evaluations against a state-of-the-art image-based virtual try-on method and ablation studies to demonstrate that our method achieves superior performance in terms of realism and consistency of the generated garment images.

Reconstructing 3D human poses from video has wide applications, such as character animation and sports analysis. Automatic 3D pose reconstruction methods have demonstrated promising results, but failure cases can still appear due to the diversity of human actions, capturing conditions, and depth ambiguities. Thus, manual intervention remains indispensable, which can be time-consuming and require professional skills. We thus present iPose, an interactive tool that facilitates intuitive human pose reconstruction from a given video. Our tool incorporates both human perception in specifying pose appearance to achieve controllability, and video frame processing algorithms to achieve precision and automation. A user manipulates the projection of a 3D pose via 2D operations on top of video frames, and the 3D poses are updated correspondingly while satisfying both kinematic and video frame constraints. The pose updates are propagated temporally to reduce user workload. We evaluate the effectiveness of iPose with a user study on the 3DPW dataset and expert interviews.

Videos are an accessible form of media for analyzing sports postures and providing feedback to athletes. Existing sport-specific systems embed bespoke human pose attributes and thus can be hard to scale for new attributes, especially for users without programming experiences. Some systems retain scalability by directly showing the differences between two poses, but they might not clearly visualize the key differences that viewers would like to pursue. Besides, video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers or reference poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints in videos, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. To address these issues, we study customizable human pose data analysis and visualization in the context of running pose attributes, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, PoseCoach , to provide feedback on running poses for amateurs by comparing the running poses between a novice and an expert. PoseCoach adopts a customizable data analysis model to allow users' controllability in defining pose attributes of their interests through our interface. To avoid the influence of viewpoint differences and provide intuitive feedback, PoseCoach visualizes the pose differences as part-based 3D animations on a human model to imitate the demonstration of a human coach. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system.