Authentic Volumetric Avatars From a Phone Scan (SIGGRAPH 2022)

Paper: Authors: Chen Cao, Tomas Simon, Jin Kyu Kim, Gabe Schwartz, Michael Zollhoefer, Shunsuke Saito, Stephen Lombardi, Shih-en Wei, Danielle Belko, Shoou-i Yu, Yaser Sheikh, Jason Saragih Abstract: Creating photorealistic avatars of existing people currently requires extensive person-specific data capture, a process that has been primarily employed in the VFX industry due to its complexity. Our work aims to address this drawback by relying only on a short mobile phone capture to obtain a drivable 3D head avatar that matches a person’s likeness faithfully. In contrast to existing approaches, our architecture avoids the complex task of directly modeling the entire manifold of human appearance, aiming instead to generate an avatar model that can be specialized to novel identities using only small amounts of data. The model dispenses with low-dimensional latent spaces that are commonly employed for hallucinating novel identities, and instead, uses a conditional representation that can extract person-specific information at multiple scales from a registered neutral phone scan. We achieve this through the use of a novel universal avatar prior that has been trained on high-quality multi-view video captures of facial performances of hundreds of human subjects. By fine-tuning the model using inverse rendering we achieve increased realism and personalize its range of motion. The output of our approach is not only a high-fidelity 3D head avatar that matches the person’s facial shape and appearance, but one that can also be driven using a jointly discovered shared global expression space with disentangled controls for gaze direction. Via a series of extensive experiments we demonstrate that our avatars are faithful representations of the subject’s likeness. Compared to other state-of-the-art methods for lightweight avatar creation, our approach exhibits superior visual quality and animateability.