CVPR 2023 - Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields

In this episode we discuss Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields by Sungheon Park, Minjung Son, Seokhwan Jang, Young Chun Ahn, Ji-Yeon Kim, Nahyup Kang. The paper presents a novel technique for training spatiotemporal neural radiance fields for dynamic scenes based on temporal interpolation of feature vectors. The proposed method includes two feature interpolation approaches, one using neural networks and another using grids. The multi-level feature interpolation network captures short-term and long-term time ranges, while the grid representation reduces training time by over 100 times compared to previous neural-net-based methods without sacrificing rendering quality. The addition of a smoothness term and concatenating static and dynamic features further improves the performance of the proposed models. The method achieves state-of-the-art performance in both rendering quality and training speed.