用参考图精准补全人体图像细节，避免衣物花纹错乱。
CompleteMe: Reference-based Human Image Completion
- 双U-Net结构+区域聚焦注意力，聚焦参考图关键区域
- 在自建基准上显著提升细节保真度与语义一致性
- 适合需要高保真人物图像修复的生成任务
现有方法在重建人体形状时表现良好，但缺乏对独特细节（如特定服装图案或标志性配饰）的保留，除非提供显式参考图像。即使最先进的基于参考的修复方法也难以准确捕捉并融合参考图中的细粒度信息。为此，我们提出CompleteMe，一种新型基于参考的人体图像补全框架。该框架采用双U-Net架构结合区域聚焦注意力（RFA）模块，显式引导模型关注参考图像中的相关区域，有效捕获细微特征并确保语义对应关系准确，显著提升完成图像的真实感与一致性。此外，我们构建了一个专为评估参考式人体图像补全任务设计的挑战性基准。大量实验表明，所提方法在视觉质量与语义一致性方面均优于现有技术。
原文摘要 · Abstract (English)
Recent methods for human image completion can reconstruct plausible body shapes but often fail to preserve unique details, such as specific clothing patterns or distinctive accessories, without explicit reference images. Even state-of-the-art reference-based inpainting approaches struggle to accurately capture and integrate fine-grained details from reference images. To address this limitation, we propose CompleteMe, a novel reference-based human image completion framework. CompleteMe employs a dual U-Net architecture combined with a Region-focused Attention (RFA) Block, which explicitly guides the model's attention toward relevant regions in reference images. This approach effectively captures fine details and ensures accurate semantic correspondence, significantly improving the fidelity and consistency of completed images. Additionally, we introduce a challenging benchmark specifically designed for evaluating reference-based human image completion tasks. Extensive experiments demonstrate that our proposed method achieves superior visual quality and semantic consistency compared to existing techniques. Project page: https://liagm.github.io/CompleteMe/
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