FlowVVTON：基于流引导的无掩码视频虚拟试穿

中文摘要 AI 辅助

视频虚拟试穿旨在将目标服装跨视频帧迁移到运动的人物身上。现有方法依赖人体解析掩码或姿态关键点，在大动作和遮挡场景下常失效，导致边界伪影和时间不一致。进一步的局限是，多数方法仅依赖注意力机制进行时间建模，缺乏显式运动监督。我们提出FlowVVTON，一种完全消除解析掩码依赖的无掩码框架。光流仅用作训练时的监督信号：在生成模型所有层应用光流扭曲潜在损失，通过在显式物理运动约束下对齐相邻帧特征，实现多尺度时间一致性。两阶段训练策略在引入流引导时间监督前建立无掩码空间对齐。在TikTokDress数据集上的实验显示，FlowVVTON大幅优于基线方法，尤其在时间一致性上较SwiftTry实现了5.7倍的VFID-R提升，且在任何阶段均无需分割掩码、姿态关键点或区域标注。

英文摘要

Video virtual try-on aims to transfer a target garment onto a moving person across video frames. Current methods rely on human parsing masks or pose keypoints that frequently fail under large motions and occlusions, causing boundary artifacts and temporal inconsistency. A further limitation is that most approaches rely solely on attention mechanisms for temporal modeling, providing no explicit motion supervision. We propose FlowVVTON, a mask-free framework that eliminates parsing mask dependency entirely. Optical flow is used solely as a training-time supervision signal: a flow-warped latent loss, applied across all layers of the generation model, enforces multi-scale temporal consistency by aligning adjacent-frame features under explicit physical motion constraints. A two-stage training strategy establishes mask-free spatial alignment before introducing flow-guided temporal supervision. Experiments on TikTokDress show that FlowVVTON outperforms baselines by substantial margins, particularly in temporal consistency (5.7$\times$ VFID-R improvement over SwiftTry), while requiring no segmentation masks, pose keypoints, or region annotations at any stage.

发表机构

• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。