从单次扫描生成动态面部资产和装备
Dynamic Facial Asset and Rig Generation from a Single Scan
摘要：
电影和游戏中使用的高保真计算机生成（CG）角色的创建需要密集的体力劳动和一套全面的面部资产，这些资产将通过复杂的硬件捕获，从而导致成本高和生产周期长。为了简化和加速这一数字化过程，我们提出了一个自动生成高质量动态面部资产的框架，包括可以随时部署艺术家进行抛光的钻机。我们的框架将单次扫描作为输入，以生成一组个性化的融合形状，动态和基于物理的纹理，以及次要的面部组件（例如，牙齿和眼球）。建立在由毛孔级细节组成的面部数据库基础上，通过对不同表情和身份的$4,000$扫描，我们采用自监督神经网络从一组模板表情中学习个性化的融合形状。我们还对身份和表情之间的联合分布进行建模，从而能够从单个中性输入扫描中推断出具有动态外观的全套个性化融合形状。我们生成的个性化面部装备资产与面部动画和渲染的尖端行业管道无缝兼容。我们证明了我们的框架是强大的和有效的推断范围广泛的新的主题，并说明引人注目的渲染结果，同时动画生成的定制的基于物理的动态纹理的脸。
英文摘要：
The creation of high-fidelity computer-generated (CG) characters used in film and gaming requires intensive manual labor and a comprehensive set of facial assets to be captured with complex hardware, resulting in high cost and long production cycles. In order to simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial assets, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic and physically-based textures, as well as secondary facial components (e.g., teeth and eyeballs). Built upon a facial database consisting of pore-level details, with over $4,000$ scans of varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of the full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with cutting-edge industry pipelines for facial animation and rendering. We demonstrate that our framework is robust and effective by inferring on a wide range of novel subjects, and illustrate compelling rendering results while animating faces with generated customized physically-based dynamic textures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
