Abstract
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: this https URL
Abstract (translated)
最近的人体图像补全方法能够重建出合理的身体形状，但常常无法在没有明确参考图片的情况下保留独特的细节，比如特定的服装图案或独特的配饰。即使是目前最先进的基于参考图的修复技术也难以准确捕捉并整合来自参考图片的细微信息。为了解决这一局限性，我们提出了一种新的基于参考图的人体图像补全框架——CompleteMe。 CompleteMe采用了一种结合双U-Net架构和区域聚焦注意力（RFA）块的方法，该方法明确引导模型关注参考图片中的相关区域。这种方法能够有效地捕捉细微细节，并确保语义对应性的一致性和准确性，从而显著提高了完成图像的保真度和一致性。 此外，我们还引入了一个专门用于评估基于参考图的人体图像补全任务的新基准测试。广泛的实验表明，与现有技术相比，我们的方法在视觉质量和语义一致性方面都达到了优越水平。 项目页面：[此链接](https://this_https_URL/)（请将URL替换为实际的网页地址）
URL
https://arxiv.org/abs/2504.20042
