NeRFlame：用于3D人脸渲染的基于FLAME的NeRF条件化
NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering
AI总结：
本文提出NeRFlame方法，将FLAME网格作为独立密度体积无缝集成到NeRF架构中，结合两者优势，实现了高质量3D人脸渲染与对外观的完全控制。
摘要：
传统的3D人脸模型基于带纹理的网格表示。其中最重要的模型之一是FLAME（Faces Learned with an Articulated Model and Expressions），它能生成完全可控的人脸网格。然而，此类模型在捕捉几何和外观细节方面存在问题。与网格表示相比，神经辐射场（NeRF）能生成极其清晰的渲染结果。但是，隐式方法难以进行动画处理，且对未见过的表情泛化能力较差。有效地控制NeRF模型以实现人脸操控并非易事。本文提出了一种名为NeRFlame的新方法，它结合了NeRF和FLAME两种方法的优势。我们的方法具备NeRF的高质量渲染能力，同时也能像FLAME一样对视觉外观提供完全控制。与使用神经网络对RGB颜色和体积密度进行建模的传统基于NeRF的结构不同，我们的方法利用FLAME网格作为独立的密度体积。因此，颜色值仅存在于FLAME网格附近。该FLAME框架被无缝集成到NeRF架构中用于预测RGB颜色，使我们的模型能够显式表示体积密度并隐式捕获RGB颜色。
英文摘要：
Traditional 3D face models are based on mesh representations with texture. One of the most important models is FLAME (Faces Learned with an Articulated Model and Expressions), which produces meshes of human faces that are fully controllable. Unfortunately, such models have problems with capturing geometric and appearance details. In contrast to mesh representation, the neural radiance field (NeRF) produces extremely sharp renders. However, implicit methods are hard to animate and do not generalize well to unseen expressions. It is not trivial to effectively control NeRF models to obtain face manipulation. The present paper proposes a novel approach, named NeRFlame, which combines the strengths of both NeRF and FLAME methods. Our method enables high-quality rendering capabilities of NeRF while also offering complete control over the visual appearance, similar to FLAME. In contrast to traditional NeRF-based structures that use neural networks for RGB color and volume density modeling, our approach utilizes the FLAME mesh as a distinct density volume. Consequently, color values exist only in the vicinity of the FLAME mesh. This FLAME framework is seamlessly incorporated into the NeRF architecture for predicting RGB colors, enabling our model to explicitly represent volume density and implicitly capture RGB colors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
