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.

翻译：神经角色模型现在能够从视频中重建出精细的几何结构与纹理，但这类模型缺乏明确的阴影与着色效果，导致在生成新视角、新姿态或进行重光照时出现伪影。由于阴影是一种全局性光学效应，且所需的二次光线投射成本高昂，将其纳入模型尤为困难。我们提出了一种基于高斯密度代理的新阴影模型，该模型用简单的解析公式替代了传统采样过程。该模型支持动态运动，并专为阴影计算设计，从而避免了与之密切相关的高斯溅射算法所需的仿射投影近似和排序步骤。结合延迟神经渲染模型，我们的高斯阴影能以极低的计算开销实现朗伯着色与阴影投射。我们证明，在存在强烈直射阳光与硬阴影的挑战性户外场景中，该方法能实现更优的重建效果，并更好地分离反照率、着色与阴影。我们的方法可在无需用户任何输入的情况下优化光照方向。相较于现有最优方法，该技术生成的新姿态中阴影伪影更少，且在新场景中的重光照效果更为逼真，为在陌生环境中摆放神经角色提供了新途径，显著提升了其应用潜力。