Rendering photorealistic and dynamically moving human heads is crucial for ensuring a pleasant and immersive experience in AR/VR and video conferencing applications. However, existing methods often struggle to model challenging facial regions (e.g., mouth interior, eyes, hair/beard), resulting in unrealistic and blurry results. In this paper, we propose {\fullname} ({\name}), a method that adopts the neural point representation as well as the neural volume rendering process and discards the predefined connectivity and hard correspondence imposed by mesh-based approaches. Specifically, the neural points are strategically constrained around the surface of the target expression via a high-resolution UV displacement map, achieving increased modeling capacity and more accurate control. We introduce three technical innovations to improve the rendering and training efficiency: a patch-wise depth-guided (shading point) sampling strategy, a lightweight radiance decoding process, and a Grid-Error-Patch (GEP) ray sampling strategy during training. By design, our {\name} is better equipped to handle topologically changing regions and thin structures while also ensuring accurate expression control when animating avatars. Experiments conducted on three subjects from the Multiface dataset demonstrate the effectiveness of our designs, outperforming previous state-of-the-art methods, especially in handling challenging facial regions.

翻译：渲染具有动态运动的高逼真人头对于确保AR/VR及视频会议应用中的愉悦沉浸体验至关重要。然而，现有方法在建模具有挑战性的面部区域（如口腔内部、眼睛、头发/胡须）时往往存在困难，导致结果不真实且模糊。本文提出{\fullname}（{\name}）方法，该方法采用神经点表征与神经体积渲染流程，摒弃了基于网格方法预定义的连接关系与刚性对应约束。具体而言，通过高分辨率UV位移图将神经点策略性地约束在目标表情表面周围，从而提升建模能力并实现更精确的控制。我们引入三项技术创新以提高渲染与训练效率：分块深度引导（着色点）采样策略、轻量级辐射度解码过程以及训练中的网格误差块（GEP）射线采样策略。通过设计，我们的{\name}方法能更好地处理拓扑变化区域与薄结构，同时在驱动虚拟化身时确保精确的表情控制。在Multiface数据集三个主体上的实验验证了所提设计的有效性，该方法在多个方面超越了现有最优方法，尤其在处理具有挑战性的面部区域时表现优异。