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：渲染逼真且动态运动的人体头部对于确保AR/VR及视频会议应用获得愉悦沉浸的体验至关重要。然而，现有方法往往难以对具有挑战性的面部区域（如口腔内部、眼睛、毛发/胡须）进行建模，导致生成结果不真实且模糊。本文提出{\fullname}（{\name}）方法，该方法采用神经点表示与神经体积渲染流程，摒弃了基于网格方法所强加的预定义连接性与硬对应关系。具体而言，神经点通过高分辨率UV位移图被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升渲染与训练效率：基于块状深度引导的（着色点）采样策略、轻量化辐射解码流程，以及训练期间采用的网格-误差-块（GEP）光线采样策略。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，同时在驱动化身动画时确保精确的表情控制。在Multiface数据集三个对象上进行的实验证明了我们设计的有效性，其性能优于先前最先进方法，尤其在处理具有挑战性的面部区域方面表现突出。