Numerous emerging deep-learning techniques have had a substantial impact on computer graphics. Among the most promising breakthroughs are the recent rise of Neural Radiance Fields (NeRFs) and Gaussian Splatting (GS). NeRFs encode the object's shape and color in neural network weights using a handful of images with known camera positions to generate novel views. In contrast, GS provides accelerated training and inference without a decrease in rendering quality by encoding the object's characteristics in a collection of Gaussian distributions. These two techniques have found many use cases in spatial computing and other domains. On the other hand, the emergence of deepfake methods has sparked considerable controversy. Such techniques can have a form of artificial intelligence-generated videos that closely mimic authentic footage. Using generative models, they can modify facial features, enabling the creation of altered identities or facial expressions that exhibit a remarkably realistic appearance to a real person. Despite these controversies, deepfake can offer a next-generation solution for avatar creation and gaming when of desirable quality. To that end, we show how to combine all these emerging technologies to obtain a more plausible outcome. Our ImplicitDeepfake1 uses the classical deepfake algorithm to modify all training images separately and then train NeRF and GS on modified faces. Such relatively simple strategies can produce plausible 3D deepfake-based avatars.

翻译：大量新兴的深度学习技术已对计算机图形学产生重大影响。其中最具突破性的进展是神经辐射场（NeRF）和高斯溅射（GS）的兴起。NeRF通过利用少量已知相机位置的图像，在神经网络权重中编码物体的形状与颜色，从而生成新视角画面。相比之下，GS通过将物体特征编码为高斯分布的集合，在不降低渲染质量的前提下实现训练与推理的加速。这两项技术在空间计算及其他领域已展现出广泛应用价值。另一方面，深度伪造方法的兴起引发了显著争议。此类技术可生成高度模仿真实影像的人工智能视频，利用生成模型修改面部特征，从而创建出与真实人物高度相似的身份或表情。尽管存在争议，当具备理想质量时，深度伪造技术可为虚拟化身创建和游戏产业提供新一代解决方案。为此，我们展示了如何融合这些新兴技术以获得更可信的结果。我们提出的ImplicitDeepfake1方法先采用经典深度伪造算法分别修改所有训练图像，再基于修改后的面部图像训练NeRF与GS。这种相对简单的策略能够生成可信的基于3D深度伪造的虚拟化身。