首个多视角动态虚拟试穿框架，支持自由姿态与视角变换
AvatarVTON: 4D Virtual Try-On for Animatable Avatars
- 无先验光学流修正，实现单图驱动的动态衣物拟合
- 非线性形变模块解耦视角与姿态影响，提升衣物真实感
- 适用于AR/VR、游戏及数字人，可自由换装且保持动作连贯
我们提出AvatarVTON，首个4D虚拟试穿框架，仅需单张店内服装图像即可生成真实试穿效果，支持自由姿态控制、新视角渲染和多样服装选择。不同于现有方法，AvatarVTON在单视图监督下实现动态衣物交互，无需多视角服装采集或物理先验。框架包含两个核心模块：(1) 反向流校正器，一种无先验的光流修正策略，稳定人体贴合并保证时间一致性；(2) 非线性形变器，将高斯映射分解为视角-姿态不变与特定成分，实现自适应非线性服装变形。为建立4D虚拟试穿基准，我们统一扩展现有基线模型以进行公平定性和定量比较。大量实验表明，AvatarVTON在保真度、多样性与动态衣物真实感方面表现优异，适用于AR/VR、游戏及数字人应用。
原文摘要 · Abstract (English)
We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view rendering, and diverse garment choices. Unlike existing methods, AvatarVTON supports dynamic garment interactions under single-view supervision, without relying on multi-view garment captures or physics priors. The framework consists of two key modules: (1) a Reciprocal Flow Rectifier, a prior-free optical-flow correction strategy that stabilizes avatar fitting and ensures temporal coherence; and (2) a Non-Linear Deformer, which decomposes Gaussian maps into view-pose-invariant and view-pose-specific components, enabling adaptive, non-linear garment deformations. To establish a benchmark for 4D virtual try-on, we extend existing baselines with unified modules for fair qualitative and quantitative comparisons. Extensive experiments show that AvatarVTON achieves high fidelity, diversity, and dynamic garment realism, making it well-suited for AR/VR, gaming, and digital-human applications.
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