LoDAvatar: Hierarchical Embedding and Selective Detail Enhancement for Adaptive Levels of Detail Gaussian Avatars

TL;DR

LoDAvatar introduces a hierarchical embedding and selective detail enhancement approach for Gaussian avatars, effectively balancing visual quality and computational efficiency in virtual reality applications.

Contribution

The paper presents a novel method for integrating levels of detail into Gaussian avatars using hierarchical embedding and selective detail enhancement techniques.

Findings

Reduces computational costs during rendering

Maintains high visual quality in Gaussian avatars

Improves runtime frame rates

Abstract

With the advancement of virtual reality, the demand for 3D human avatars is increasing. The emergence of Gaussian Splatting technology has enabled the rendering of Gaussian avatars with superior visual quality and reduced computational costs. Despite numerous methods researchers propose for implementing drivable Gaussian avatars, limited attention has been given to balancing visual quality and computational costs. In this paper, we introduce LoDAvatar, a method that introduces levels of detail into Gaussian avatars through hierarchical embedding and selective detail enhancement methods. The key steps of LoDAvatar encompass data preparation, Gaussian embedding, Gaussian optimization, and selective detail enhancement. We conducted experiments involving Gaussian avatars at various levels of detail, employing both objective assessments and subjective evaluations. The outcomes indicate that incorporating levels of detail into Gaussian avatars can decrease computational costs during rendering while upholding commendable visual quality, thereby enhancing runtime frame rates. We advocate adopting LoDAvatar to render multiple dynamic Gaussian avatars or extensive Gaussian scenes to balance visual quality and computational costs.