Instead of training asingle model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame.

Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality.

Despite advancements in Neural Implicit models for 3D surface reconstruction, handling dynamic environments with interactions between arbitrary rigid, nonrigid, or deformable entities remains challenging.

Dynamic scene reconstruction is a long-term challenge in the field of 3D vision.