UFO-4D：从两幅图像中进行无约束前馈4D重建
UFO-4D: Unposed Feedforward 4D Reconstruction from Two Images
AI总结：
UFO-4D通过统一的前馈框架，从两幅图像中高效重建密集4D表示，实现3D几何、运动和相机姿态的联合估计，提升4D重建精度与效率。
摘要：
从无约束图像中进行密集4D重建仍然是一个关键挑战，当前方法依赖于缓慢的测试时间优化或碎片化的、任务特定的前馈模型。我们引入了UFO-4D，一种统一的前馈框架，能够仅从一对无约束图像中重建出密集且明确的4D表示。UFO-4D直接估计动态3D高斯溅射，使在前馈方式下能够联合且一致地估计3D几何、3D运动和相机姿态。我们的核心见解是，从单一动态3D高斯表示中可微地渲染多种信号提供了显著的训练优势。这种方法使能够实现自监督图像合成损失，同时紧密耦合外观、深度和运动。由于所有模态共享相同的几何原语，监督一个会自动正则化并改进其他部分。这种协同作用克服了数据稀缺问题，使UFO-4D在联合几何、运动和相机姿态估计上比先前工作高出3倍。我们的表示还能够在新视角和时间上实现高保真度的4D插值。请访问我们的项目页面查看视觉结果：https://ufo-4d.github.io/
英文摘要：
Dense 4D reconstruction from unposed images remains a critical challenge, with current methods relying on slow test-time optimization or fragmented, task-specific feedforward models. We introduce UFO-4D, a unified feedforward framework to reconstruct a dense, explicit 4D representation from just a pair of unposed images. UFO-4D directly estimates dynamic 3D Gaussian Splats, enabling the joint and consistent estimation of 3D geometry, 3D motion, and camera pose in a feedforward manner. Our core insight is that differentiably rendering multiple signals from a single Dynamic 3D Gaussian representation offers major training advantages. This approach enables a self-supervised image synthesis loss while tightly coupling appearance, depth, and motion. Since all modalities share the same geometric primitives, supervising one inherently regularizes and improves the others. This synergy overcomes data scarcity, allowing UFO-4D to outperform prior work by up to 3 times in joint geometry, motion, and camera pose estimation. Our representation also enables high-fidelity 4D interpolation across novel views and time. Please visit our project page for visual results: https://ufo-4d.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
