Abstract
Amodal completion, generating invisible parts of occluded objects, is vital for applications like image editing and AR. Prior methods face challenges with data needs, generalization, or error accumulation in progressive pipelines. We propose a Collaborative Multi-Agent Reasoning Framework based on upfront collaborative reasoning to overcome these issues. Our framework uses multiple agents to collaboratively analyze occlusion relationships and determine necessary boundary expansion, yielding a precise mask for inpainting. Concurrently, an agent generates fine-grained textual descriptions, enabling Fine-Grained Semantic Guidance. This ensures accurate object synthesis and prevents the regeneration of occluders or other unwanted elements, especially within large inpainting areas. Furthermore, our method directly produces layered RGBA outputs guided by visible masks and attention maps from a Diffusion Transformer, eliminating extra segmentation. Extensive evaluations demonstrate our framework achieves state-of-the-art visual quality.
Abstract (translated)
非模态完成（生成被遮挡物体的不可见部分）对于图像编辑和AR等应用至关重要。然而，之前的方法在数据需求、泛化能力或逐步处理流水线中的错误累积方面面临挑战。我们提出了一种基于前期协作推理的多代理协同推理框架，以克服这些难题。我们的框架利用多个代理来共同分析遮挡关系并确定必要的边界扩展，从而生成用于图像修复的精确掩码。同时，一个代理会生成细粒度的文字描述，这提供了精细语义指导，确保准确的对象合成，并防止重新生成遮挡物或其他不必要的元素，特别是在大范围的修复区域中更为关键。 此外，我们的方法直接根据可见掩码和来自扩散变换器的注意力图生成分层RGBA输出，从而省去了额外的分割步骤。广泛的评估表明，我们的框架在视觉质量上达到了最先进的水平。
URL
https://arxiv.org/abs/2509.17757
