Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis

By Zhan Li, Zhang Chen, Zhong Li. Computer Vision and Pattern Recognition, 2023

419 citations recorded by the source corpus.

Novel view synthesis of dynamic scenes has been an intriguing yet challenging problem. Despite recent advancements, simultaneously achieving high-resolution photorealistic results, real-time rendering, and compact storage remains a formidable task. To address these challenges, we propose Spacetime Gaussian Feature Splatting as a novel dynamic scene representation, composed of three pivotal components. First, we formulate expressive Spacetime Gaussians by enhancing 3D Gaussians with temporal opacity and parametric motion/rotation. This enables Space-time Gaussians to capture static, dynamic, as well as transient content within a scene. Second, we introduce splatted feature rendering, which replaces spherical harmonics with neural features. These features facilitate the modeling of view- and time-dependent appearance while maintaining small size. Third, we leverage the guidance of training error and coarse depth to sample new Gaussians in areas that are challenging to converge with existing pipelines. Experiments on several established real-world datasets demonstrate that our method achieves state-of-the-art rendering quality and speed, while retaining compact storage. At 8K resolution, our lite-version model can render at 60 FPS on an Nvidia RTX 4090 GPU.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/29ea8f3648264f4fa8dec627fc8726131ea72cdc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Xinggang Wang
  2. Qi Tian
  3. Lingxi Xie
  4. Wenyu Liu
  5. Xiaopeng Zhang
  6. Alan L. Yuille
  7. Yi Yang
  8. Luc Van Gool

Citations in the corpus