SWAGS: Sampling Windows Adaptively for Dynamic 3D Gaussian Splatting
Richard Shaw Jifei Song Arthur Moreau Michal Nazarczuk Sibi Catley-Chandar Helisa Dhamo Eduardo Perez-Pellitero
Novel view synthesis has shown rapid progress recently, with methods capable
of producing evermore photo-realistic results. 3D Gaussian Splatting has
emerged as a particularly promising method, producing high-quality renderings
of static scenes and enabling interactive viewing at real-time frame rates.
However, it is currently limited to static scenes only. In this work, we extend
3D Gaussian Splatting to reconstruct dynamic scenes. We model the dynamics of a
scene using a tunable MLP, which learns the deformation field from a canonical
space to a set of 3D Gaussians per frame. To disentangle the static and dynamic
parts of the scene, we learn a tuneable parameter for each Gaussian, which
weighs the respective MLP parameters to focus attention on the dynamic parts.
This improves the model's ability to capture dynamics in scenes with an
imbalance of static to dynamic regions. To handle scenes of arbitrary length
whilst maintaining high rendering quality, we introduce an adaptive window
sampling strategy to partition the sequence into windows based on the amount of
movement in the sequence. We train a separate dynamic Gaussian Splatting model
for each window, allowing the canonical representation to change, thus enabling
the reconstruction of scenes with significant geometric or topological changes.
Temporal consistency is enforced using a fine-tuning step with self-supervising
consistency loss on randomly sampled novel views. As a result, our method
produces high-quality renderings of general dynamic scenes with competitive
quantitative performance, which can be viewed in real-time with our dynamic
interactive viewer.