Neural character models can now reconstruct detailed geometry and texture from video, but they lack explicit shadows and shading, leading to artifacts when generating novel views and poses or during relighting. It is particularly difficult to include shadows as they are a global effect and the required casting of secondary rays is costly. We propose a new shadow model using a Gaussian density proxy that replaces sampling with a simple analytic formula. It supports dynamic motion and is tailored for shadow computation, thereby avoiding the affine projection approximation and sorting required by the closely related Gaussian splatting. Combined with a deferred neural rendering model, our Gaussian shadows enable Lambertian shading and shadow casting with minimal overhead. We demonstrate improved reconstructions, with better separation of albedo, shading, and shadows in challenging outdoor scenes with direct sun light and hard shadows. Our method is able to optimize the light direction without any input from the user. As a result, novel poses have fewer shadow artifacts and relighting in novel scenes is more realistic compared to the state-of-the-art methods, providing new ways to pose neural characters in novel environments, increasing their applicability.
神经角色模型现在可以从视频中重建详细的几何形状和纹理，但它们缺乏明确的阴影和着色，导致在生成新视角和姿态或在重新照明时出现失真。特别是包含阴影非常困难，因为阴影是一种全局效应，且所需的次级射线投射成本很高。我们提出了一种新的阴影模型，使用高斯密度代理替代采样，采用简单的分析公式。它支持动态运动，专为阴影计算量身定制，从而避免了与密切相关的高斯喷溅所需的仿射投影近似和排序。结合使用了延迟神经渲染模型，我们的高斯阴影支持兰伯特着色和阴影投射，且额外开销最小。我们展示了改进的重建结果，在具有直接阳光和硬阴影的挑战性户外场景中，反照率、着色和阴影的分离更好。我们的方法能够在没有任何用户输入的情况下优化光线方向。因此，新的姿态有更少的阴影失真，而且在新场景中的重新照明比现有最先进方法更加逼真，为在新环境中摆放神经角色提供了新的方式，增加了它们的适用性。