ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On
Jinjuan Wang, Wenzhang Sun, Ming Li, Yun Zheng, Fanyao Li, Zhulin Tao, Donglin Di, Hao Li, Wei Chen, Xianglin Huang

TL;DR
ChronoTailor is a diffusion-based video virtual try-on framework that employs attention guidance to achieve temporally consistent, detailed garment transfer in videos, addressing previous issues of continuity and detail preservation.
Contribution
It introduces a novel spatio-temporal attention mechanism and multi-scale garment feature integration for improved video virtual try-on performance.
Findings
Outperforms previous methods in maintaining temporal continuity
Preserves fine-grained garment details during motion
Successfully handles complex dynamic scenes
Abstract
Video virtual try-on aims to seamlessly replace the clothing of a person in a source video with a target garment. Despite significant progress in this field, existing approaches still struggle to maintain continuity and reproduce garment details. In this paper, we introduce ChronoTailor, a diffusion-based framework that generates temporally consistent videos while preserving fine-grained garment details. By employing a precise spatio-temporal attention mechanism to guide the integration of fine-grained garment features, ChronoTailor achieves robust try-on performance. First, ChronoTailor leverages region-aware spatial guidance to steer the evolution of spatial attention and employs an attention-driven temporal feature fusion mechanism to generate more continuous temporal features. This dual approach not only enables fine-grained local editing but also effectively mitigates artifacts…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis · Face recognition and analysis
