UMI3D：通过同步聚焦交叉注意力路由在无约束多图像输入上进行稳健的3D生成
UMI3D: Robust 3D Generation on Unconstrained Multi-Image Inputs via Simultaneous Focus Cross-Attention Routing
AI总结：
研究针对3D基础模型在无约束多图像输入上表现不佳的问题，提出UMI3D框架，通过同步聚焦交叉注意力路由，利用体素参考分数，无需训练即可提升单图像3D生成框架在多图像输入时的性能。
AI中文摘要：
近期的3D基础模型能从单张图像生成高质量资产，但在无约束多图像输入上表现显著下降。本文认为问题源于单图像交叉注意力与多图像设置不匹配，现有模型缺乏在每个去噪步骤中决定每个3D体素应信赖哪张图像的原则方法。通过重新审视单图像3D基础模型，发现将每个体素明确路由到最具信息的图像足以在不一致的多图像输入上解锁强大性能。基于此提出UMI3D，一个无需训练且即插即用的框架，其核心同时聚焦交叉注意力（SFC-Attn）在每个去噪步骤激活所有条件图像，同时允许每个体素聚焦于最能解释它的单张图像。为实现这种路由，推导了体素参考分数（VRS）。大量实验表明UMI3D在各种任务中解锁了单图像3D生成框架的多图像潜力。
英文摘要：
Recent 3D foundation models can generate high-quality assets from a single image, but degrade markedly on unconstrained multi-image inputs, often producing distorted geometry, over-smoothed textures, and chaotic colors. We argue that this failure stems not from limited model capacity, but from a mismatch between single-image cross-attention and the multi-image setting: existing models lack a principled way to decide which image each 3D voxel should trust at each denoising step. Revisiting recent single-image 3D foundation models, we show that explicitly routing each voxel to its most informative image is sufficient to unlock strong performance on inconsistent multi-image inputs. Based on this observation, we propose UMI3D, a training-free and plug-and-play framework that restructures cross-attention for unconstrained multi-image 3D generation. Its core, Simultaneous Focus Cross-Attention (SFC-Attn), activates all conditioning images at each denoising step while allowing each voxel to focus on the single image that best explains it. To enable this routing, we derive the Voxel Reference Score (VRS), a model-intrinsic metric for voxel--image affinity that requires no external matching, segmentation, or correspondence models. Extensive experiments show that UMI3D unlocks the multi-image potential of single-image 3D generation frameworks across diverse tasks. Project Page: UMI3D-Project.github.io.
