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.

提出一种将神经点表示和神经体渲染结合的方法，使用高分辨率 UV 位移贴图约束神经点来实现更准确的表情控制，通过使用 GEP 射线采样策略和轻量级辐射解码过程，提高了渲染和训练效率。在 Multiface 数据集上通过实验验证了该方法的有效性。

基于神经网络点的体积化头像：表面引导神经点用于高效和逼真的体积化头像

Neural Point-based Volumetric Avatar: Surface-guided Neural Points for  Efficient and Photorealistic Volumetric Head Avatar

Media forensics has attracted a lot of attention in the last years in part
due to the increasing concerns around DeepFakes. Since the initial DeepFake
databases from the 1st generation such as UADFV and FaceForensics++ up to the
latest databases of the 2nd generation such as Celeb-DF and DFDC, many visual
improvements have been carried out, making fake videos almost indistinguishable
to the human eye. This study provides an exhaustive analysis of both 1st and
2nd DeepFake generations in terms of facial regions and fake detection
performance. Two different methods are considered in our experimental
framework: i) the traditional one followed in the literature and based on
selecting the entire face as input to the fake detection system, and ii) a
novel approach based on the selection of specific facial regions as input to
the fake detection system.
Among all the findings resulting from our experiments, we highlight the poor
fake detection results achieved even by the strongest state-of-the-art fake
detectors in the latest DeepFake databases of the 2nd generation, with Equal
Error Rate results ranging from 15% to 30%. These results remark the necessity
of further research to develop more sophisticated fake detectors.

该研究对第一代和第二代 DeepFake 进行了详尽的面部区域和伪造检测性能分析，在最新的 DeepFake 数据库的第二代中，即使是最强大的最先进的伪造检测器也无法达到理想的结果，证明了进一步研究开发更精密的伪造检测器的必要性。