Implicit Neural Representations for Facial Animation

面部动画的隐式神经表示

• 批准号: 2889954
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889954
• 关键词: Implicit; Neural; Representations; Facial; Animation

Neural Radiance Fields (NeRFs) are a type of implicit neural representation that uses neural networks to model complex visual data without explicitly defining their parameters. In recent years, they have emerged as a leading solution in 3D graphics and asset generation. Their growing prominence extends beyond static 3D modelling and is increasingly embraced by the animation and virtual reality (VR) communities, primarily due to their ability to balance high compressibility and exceptional reconstruction accuracy. NeRFs hold substantial promise in a variety of applications, and one fascinating domain is the field of Facial Animation.Despite their merits, NeRFs come with their set of challenges and we aim to achieve the following research objectives: 1. Improvement in Explainability and Controllability: NeRFs operate as "black box" models and their opacity makes it difficult to understand and control the specific physical attributes of the output. In the context of facial animation, this could lead to suboptimal control over the appearance and emotions conveyed by animated avatars. To this end, we will develop disentangled representations of facial identity and expressions to improve controllability. 2. Integration of Speech: Achieving multimodal synchronization between visual and auditory cues is a challenging and ongoing endeavour. We will develop models that will not only capture the visual dynamics of facial expressions but also synchronize them with the phonetic elements of speech. 3. Watermarking: NeRFs and similar generative models have raised concerns about their potential misuse, especially in the creation of "deepfake" content. Deepfakes can be used to manipulate images and videos to a high degree of realism, posing risks to privacy, security, and misinformation. As a response, we aim to develop robust watermarking and authentication techniques that are crucial to detect and mitigate "deepfake" content. Animated human avatars have a wide array of applications - rich VR experiences, teleconferencing, gaming, and online education. The integration of NeRF-based facial animation would elevate the level of realism, resulting in an immersive VR environment. Moreover, the data-driven approach would streamline workflows by eliminating the need for artists to spend hours on manual avatar design. This would not only enhance the quality of digital experiences but also accelerate creative processes, making NeRFs an invaluable asset for artists, developers, and users alike. Our research on developing neural representations for facial animation directly aligns with EPSRC's interest in "Image and Vision Computing." In recent years, generative models have become the technological "holy grail" for industry and academia alike and EPSRC has been funding various research projects under this theme across the country, counting on the massive potential of AI models. By advancing the state-of-the-art in NeRFs for facial animation, our research would expand the boundaries of 3D vision and graphics. The outcomes of this research promise enhanced visual realism and multimodal interactivity, with broad implications for the future of human-computer interaction and digital communication, fully reflecting EPSRC's commitment to advancing innovations in the field.

神经辐射场（NERFS）是一种隐式神经表示，它使用神经网络在不明确定义其参数的情况下对复杂的视觉数据进行建模。近年来，它们已成为3D图形和资产产生的领先解决方案。它们日益增长的突出性超出了静态3D建模，并且越来越多地受到动画和虚拟现实（VR）社区的影响，这主要是由于它们能够平衡高可压缩性和出色的重建精度。 NERFS在各种应用中都有巨大的希望，一个有趣的领域是面部动画领域。尽管如此，NERF带来了一系列挑战，我们旨在实现以下研究目标：1。改善解释性和控制性能力：NERFS的模型及其出色的型号及其难以理解和控制特定的物理属性属于属性和控制属性，并能够获得特定的属性。在面部动画的背景下，这可能导致对动画化身传达的外观和情感的次优控制。为此，我们将开发面部身份和表达式的分离表示，以提高可控性。 2。语音的整合：在视觉和听觉线索之间实现多模式同步是一项具有挑战性和持续的努力。我们将开发模型，不仅将捕获面部表情的视觉动力学，而且还将它们与语音元素同步。 3。水印：NERF和类似的生成模型引起了人们对它们潜在滥用的担忧，尤其是在创建“ Deepfake”含量的情况下。 Deepfakes可以用来以高度现实主义的方式来操纵图像和视频，对隐私，安全性和错误信息构成风险。作为回应，我们旨在开发可靠的水印和身份验证技术，这些技术对于检测和减轻“深泡”内容至关重要。动画人类化身具有广泛的应用 - 丰富的VR体验，电视会议，游戏和在线教育。基于NERF的面部动画的整合将提高现实主义的水平，从而带来沉浸式的VR环境。此外，数据驱动的方法将通过消除艺术家在手动化身设计上花费数小时的需求来简化工作流程。这不仅可以提高数字体验的质量，还可以加快创作流程，使Nerfs成为艺术家，开发人员和用户的宝贵资产。我们关于开发面部动画神经表示的研究直接与EPSRC对“图像和视觉计算”的兴趣保持一致。近年来，生成模型已成为行业和学术界的技术“圣杯”，EPSRC一直在全国各地为此主题下的各种研究项目提供资金，依靠AI模型的巨大潜力。通过推进Nerfs的最新面部动画，我们的研究将扩大3D视觉和图形的边界。这项研究的结果有望增强视觉现实主义和多模式的互动，对人类计算机互动和数字通信的未来产生了广泛的影响，完全反映了EPSRC致力于推进该领域的创新。