Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos

Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectively handle dynamic content. We present BTimer (short for BulletTimer), the first motion-aware feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes. Our approach reconstructs the full scene in a 3D Gaussian Splatting representation at a given target ('bullet') timestamp by aggregating information from all the context frames. Such a formulation allows BTimer to gain scalability and generalization by leveraging both static and dynamic scene datasets. Given a casual monocular dynamic video, BTimer reconstructs a bullet-time 1 scene within 150ms resolution while reaching state-ofthe-art performance on both static and dynamic scene datasets, even compared with optimization-based approaches. * / † : Equal contribution/advising. 1 In this paper, we define bullet-time as the instantiation of a 3D scene frozen at a given/fixed timestamp t.