Delving into Asymmetric Information Dynamics for High-Fidelity Virtual Try-On

2026-09-22

AI Summary

针对虚拟试衣中纹理退化和结构漂移问题，通过分析不对称信息动态并提出RealFit框架，结合单向信息流与解耦时间步调制方法来解决。

Abstract

Virtual try-on (VTON) requires precise pixel-level fidelity, yet mainstream Diffusion Transformers (DiTs) often suffer from texture degradation and structural drift. We identify symmetric interactions in standard joint-attention mechanisms as a source of these failures. Although such interactions support semantic flexibility in general-purpose editing, they allow stochastic noise to corrupt deterministic garment features in VTON. We analyze this problem through asymmetric information dynamics and introduce two diagnostic indicators: Conditional Attention Entropy (CAE) for feature unbiasedness and Injected Information Flux (IIF) for injection effectiveness. Our analysis suggests that symmetric bidirectional attention can corrupt conditional features and attenuate the conditional signal. To address these limitations, we propose RealFit, a framework that combines Unidirectional Information Flow (UIF) with Decoupled Timestep Modulation (DTM). UIF isolates the garment condition from stochastic noise to preserve garment identity, while DTM optimizes the modulation scale to maintain a strong conditional signal. The resulting time-invariant condition branch enables a conditional KV cache that reduces inference time by approximately 75%. RealFit offers a principled approach to conditional generation with state-of-the-art fidelity and efficiency.

Problem

• Virtual Try-On
• Diffusion Transformers
• Texture Degradation
• Structural Drift

Innovation

• Unidirectional Information Flow (UIF)
• Decoupled Timestep Modulation (DTM)
• Conditional KV cache