IntrinsicNGP: Intrinsic Coordinate based Hash Encoding for Human NeRF

TL;DR

IntrinsicNGP introduces an intrinsic coordinate-based hash encoding for human NeRFs, enabling rapid training from scratch in minutes and allowing shape editing, thus improving efficiency and flexibility in human view synthesis.

Contribution

It proposes a novel intrinsic coordinate system for hash encoding in NeRFs, facilitating fast training and shape editing for human performers.

Findings

Achieves high-fidelity results in minutes from videos.

Effectively aggregates inter-frame information using proxy geometry.

Demonstrates shape editing capabilities of reconstructed subjects.

Abstract

Recently, many works have been proposed to utilize the neural radiance field for novel view synthesis of human performers. However, most of these methods require hours of training, making them difficult for practical use. To address this challenging problem, we propose IntrinsicNGP, which can train from scratch and achieve high-fidelity results in few minutes with videos of a human performer. To achieve this target, we introduce a continuous and optimizable intrinsic coordinate rather than the original explicit Euclidean coordinate in the hash encoding module of instant-NGP. With this novel intrinsic coordinate, IntrinsicNGP can aggregate inter-frame information for dynamic objects with the help of proxy geometry shapes. Moreover, the results trained with the given rough geometry shapes can be further refined with an optimizable offset field based on the intrinsic coordinate.Extensive experimental results on several datasets demonstrate the effectiveness and efficiency of IntrinsicNGP. We also illustrate our approach's ability to edit the shape of reconstructed subjects.