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.

使用 3D 高斯喷洒法重构动态场景，通过可调整的多层感知器（MLP）模型将场景的动态部分与静态部分分离，引入自适应窗口采样策略和自监督一致性损失来实现场景的时域一致性和高质量的可视化，达到了与竞争性能的动态场景高品质实时渲染。