Domain Engineering for Applied Monocular Reconstruction of Parametric Faces
This addresses the challenge of manually creating realistic avatars for online 3D applications and video games, though it appears incremental as it builds on existing parametric models.
The paper tackles the Face-to-Parameters problem by reconstructing parametric faces from single images using synthetic data and domain adaptation, improving accuracy and enabling more efficient model training in industrial applications.
Many modern online 3D applications and video games rely on parametric models of human faces for creating believable avatars. However, manually reproducing someone's facial likeness with a parametric model is difficult and time-consuming. Machine Learning solution for that task is highly desirable but is also challenging. The paper proposes a novel approach to the so-called Face-to-Parameters problem (F2P for short), aiming to reconstruct a parametric face from a single image. The proposed method utilizes synthetic data, domain decomposition, and domain adaptation to address multifaceted challenges in solving the F2P. The open-sourced codebase illustrates our key observations and provides means for quantitative evaluation. The presented approach proves practical in an industrial application; it improves accuracy and allows for more efficient models training. The techniques have the potential to extend to other types of parametric models.