高斯頭像：通過動態高斯實現超高保真頭像

摘要

在輕量稀疏視角設置下，創建高保真度的3D頭像一直是研究的熱點，但仍然存在著巨大挑戰。本文提出了高保真度頭像建模的高斯頭像方法，使用可控的3D高斯模型來表示。我們優化了中性的3D高斯模型，並通過完全學習的基於MLP的變形場來捕捉複雜的表情。這兩部分互相補充，因此我們的方法可以在確保表情準確性的同時模擬細緻的動態細節。此外，我們設計了一個基於隱式SDF和深度行進四面體的幾何引導初始化策略，以確保訓練過程的穩定性和收斂性。實驗表明，我們的方法優於其他最先進的稀疏視角方法，在2K分辨率下甚至在夸張的表情下實現了超高保真度的渲染質量。

English

Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.

高斯頭像：通過動態高斯實現超高保真頭像

Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians

摘要

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