  Dynamic novel view synthesis aims to capture the temporal evolution of visual
content within videos. Existing methods struggle to distinguishing between
motion and structure, particularly in scenarios where camera poses are either
unknown or constrained compared to object motion. Furthermore, with information
solely from reference images, it is extremely challenging to hallucinate unseen
regions that are occluded or partially observed in the given videos. To address
these issues, we first finetune a pretrained RGB-D diffusion model on the video
frames using a customization technique. Subsequently, we distill the knowledge
from the finetuned model to a 4D representations encompassing both dynamic and
static Neural Radiance Fields (NeRF) components. The proposed pipeline achieves
geometric consistency while preserving the scene identity. We perform thorough
experiments to evaluate the efficacy of the proposed method qualitatively and
quantitatively. Our results demonstrate the robustness and utility of our
approach in challenging cases, further advancing dynamic novel view synthesis.
