  We present a novel neural radiance model that is trainable in a
self-supervised manner for novel-view synthesis of dynamic unstructured scenes.
Our end-to-end trainable algorithm learns highly complex, real-world static
scenes within seconds and dynamic scenes with both rigid and non-rigid motion
within minutes. By differentiating between static and motion-centric pixels, we
create high-quality representations from a sparse set of images. We perform
extensive qualitative and quantitative evaluation on existing benchmarks and
set the state-of-the-art on performance measures on the challenging NVIDIA
Dynamic Scenes Dataset. Additionally, we evaluate our model performance on
challenging real-world datasets such as Cholec80 and SurgicalActions160.
