Streaming Radiance Fields for 3D Video Synthesis

1 code implementation • 26 Oct 2022 • Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan

Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame.

Incremental Learning Model Optimization +1

Compressing Volumetric Radiance Fields to 1 MB

1 code implementation • CVPR 2023 • Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Liefeng Bo

Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering.

Model Compression Neural Rendering +1

4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions

1 code implementation • 9 Dec 2022 • Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, Liefeng Bo

In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on the methodology of neural radiance fields (NeRF).

4k Vocal Bursts Intensity Prediction

Compact Real-time Radiance Fields with Neural Codebook

no code implementations • 29 May 2023 • Lingzhi Li, Zhongshu Wang, Zhen Shen, Li Shen, Ping Tan

Reconstructing neural radiance fields with explicit volumetric representations, demonstrated by Plenoxels, has shown remarkable advantages on training and rendering efficiency, while grid-based representations typically induce considerable overhead for storage and transmission.