用神经显式表面实现快速逼真的人体光照重渲染。
Fast and Physically-based Neural Explicit Surface for Relightable Human Avatars
- 用2D神经材质图替代传统体素，压缩表示空间。
- 支持实时物理光照渲染，速度远超现有方法。
- 适合需要高质量人体虚拟形象的AR/VR应用。
从稀疏视角视频高效建模可重光照的人体虚拟形象对AR/VR应用至关重要。现有方法采用神经隐式表示捕捉动态几何与反射特性，但因体渲染需密集采样而计算成本高。为此，我们提出基于物理的神经显式表面（PhyNES），采用紧凑的神经材质图，基于神经显式表面（NES）表示。PhyNES将人体模型组织在紧凑的二维空间中，提升材质解耦效率。通过将有符号距离场与显式表面连接，实现参数化人体形状模型附近的高效几何推断。该方法将动态几何、纹理和材质图建模为2D神经表示，支持高效光栅化。PhyNES能有效捕捉不同光照下的物理表面属性，实现实时物理基础渲染。实验表明，PhyNES在重光照质量上达到最先进水平，同时显著提升渲染速度、内存效率和重建质量。
原文摘要 · Abstract (English)
Efficiently modeling relightable human avatars from sparse-view videos is crucial for AR/VR applications. Current methods use neural implicit representations to capture dynamic geometry and reflectance, which incur high costs due to the need for dense sampling in volume rendering. To overcome these challenges, we introduce Physically-based Neural Explicit Surface (PhyNES), which employs compact neural material maps based on the Neural Explicit Surface (NES) representation. PhyNES organizes human models in a compact 2D space, enhancing material disentanglement efficiency. By connecting Signed Distance Fields to explicit surfaces, PhyNES enables efficient geometry inference around a parameterized human shape model. This approach models dynamic geometry, texture, and material maps as 2D neural representations, enabling efficient rasterization. PhyNES effectively captures physical surface attributes under varying illumination, enabling real-time physically-based rendering. Experiments show that PhyNES achieves relighting quality comparable to SOTA methods while significantly improving rendering speed, memory efficiency, and reconstruction quality.
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