LoDAvatar: Hierarchical Embedding and Adaptive Levels of Detail with Gaussian Splatting for Enhanced Human Avatars
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.
随着虚拟现实的进步，对三维人类头像的需求日益增长。高斯喷涂技术的出现使得渲染高斯头像具有出色的视觉质量和较低的计算成本。尽管研究者提出了多种可驱动高斯头像的方法，但在平衡视觉质量和计算成本方面的研究较为有限。本文提出了 LoDAvatar，一种通过分层嵌入和选择性细节增强方法在高斯头像中引入细节层次的方法。 LoDAvatar 的关键步骤包括数据准备、高斯嵌入、高斯优化以及选择性细节增强。我们对不同细节层次的高斯头像进行了实验，采用了客观评估和主观评价。结果表明，在高斯头像中引入细节层次可以在渲染过程中降低计算成本，同时保持出色的视觉质量，从而提升运行时的帧率。我们倡导采用 LoDAvatar 进行多个动态高斯头像或大规模高斯场景的渲染，以平衡视觉质量和计算成本。