Motion from Blur: Intra-Exposure Trajectory Recovery for Feed-Forward 3D Deblurring

Recent feed-forward models estimate camera poses and scene geometry from multi-view images in a single forward pass, enabling fast 3D reconstruction. However, their performance degrades markedly on motion-blurred inputs. We find that their pose estimates exhibit a systematic bias aligned with the motion-blur direction, which persists even after fine-tuning on blurred data, revealing a mismatch between single-pose prediction and trajectory-based blur formation. We therefore propose MoB3R, a feed-forward framework that recovers the intra-exposure camera trajectory and uses it to reconstruct sharp 3D Gaussians. Rather than treating blur only as an artifact, MoB3R treats its spatial pattern as a signal of camera motion: the geometry stage recovers exposure trajectories with additional motion tokens, and the appearance stage derives a per-pixel motion field to guide sharp reconstruction through cross-attention, supervised by a cycle reblur loss. Experiments on synthetic and real blurred scenes show improved pose estimation and novel-view rendering across blur levels, while reconstructing in seconds rather than the hours required by per-scene optimization.