Existing methods for Virtual Try-On (VTON) often struggle to preserve fine garment details, especially in unpaired settings where accurate person-garment correspondence is required. These methods do not explicitly enforce person-garment alignment and fail to explain how correspondence emerges within Diffusion Transformers (DiTs). In this paper, we first analyze full 3D attention in DiT-based architecture and reveal that the person-garment correspondence critically depends on precise person-garment query-key matching within the full 3D attention. Building on this insight, we then introduce CORrespondence ALignment (CORAL), a DiT-based framework that explicitly aligns query-key matching with robust external correspondences. CORAL integrates two complementary components: a correspondence distillation loss that aligns reliable matches with person-garment attention, and an entropy minimization loss that sharpens the attention distribution. We further propose a VLM-based evaluation protocol to better reflect human preference. CORAL consistently improves over the baseline, enhancing both global shape transfer and local detail preservation. Extensive ablations validate our design choices.

翻译：现有虚拟试穿方法往往难以保持服装的精细细节，尤其是在非配对场景下需要精确建立人体-服装对应关系时。这些方法未显式强化人体-服装对齐机制，也无法解释对应关系在扩散Transformer中的形成机理。本文首先分析了基于DiT架构中的全三维注意力机制，揭示了人体-服装对应关系本质上依赖于全三维注意力中人体与服装查询-键值的精确匹配。基于此发现，我们提出了对应关系对齐框架CORAL，该DiT框架通过鲁棒的外部对应关系显式对齐查询-键值匹配。CORAL整合了两个互补组件：将可靠匹配关系与人体-服装注意力对齐的对应蒸馏损失，以及锐化注意力分布的熵最小化损失。我们进一步提出了基于视觉语言模型的评估方案以更好地反映人类偏好。CORAL在基线模型上实现了持续改进，同时提升了整体形状迁移和局部细节保持能力。大量消融实验验证了我们设计方案的有效性。