We propose a novel approach for 3D video synthesis that is able to represent multi-view
video recordings of a dynamic real-world scene in a compact, yet expressive representation …

The recent research explosion around implicit neural representations, such as NeRF, shows
that there is immense potential for implicitly storing high‐quality scene and lighting …

Synthesizing high-fidelity videos from real-world multiview input is challenging due to the
complexities of real-world environments and high-dynamic movements. Previous works …

Recent research explosion on Neural Radiance Field (NeRF) shows the encouraging
potential to represent complex scenes with neural networks. One major drawback of NeRF is …

We present a real-time neural radiance caching method for path-traced global illumination.
Our system is designed to handle fully dynamic scenes, and makes no assumptions about …

Temporal interpolation often plays a crucial role to learn meaningful representations in
dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural …

Gaussian Splatting has emerged as a prominent model for constructing 3D representations
from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting …

Succinct representation of complex signals using coordinate-based neural representations
(CNRs) has seen great progress, and several recent efforts focus on extending them for …

Abstract 3D Gaussian Splatting showcases notable advancements in photo-realistic and
real-time novel view synthesis. However, it faces challenges in modeling mirror reflections …

This work targets at using a general deep learning framework to synthesize free-viewpoint
images of arbitrary human performers, only requiring a sparse number of camera views as …