Learning Formation of Physically-Based Face Attributes
This work addresses the need for anatomically correct and diverse facial models in graphics and vision applications, though it is incremental as it builds on existing morphable model techniques.
The paper tackles the problem of generating high-resolution, physically-based facial assets by introducing a non-linear morphable face model that learns to correlate albedo and geometry from 4000 facial scans, producing pore-level resolution geometry and material attributes for rendering.
Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering. We aim to maximize the variety of face identities, while increasing the robustness of correspondence between unique components, including middle-frequency geometry, albedo maps, specular intensity maps and high-frequency displacement details. Our deep learning based generative model learns to correlate albedo and geometry, which ensures the anatomical correctness of the generated assets. We demonstrate potential use of our generative model for novel identity generation, model fitting, interpolation, animation, high fidelity data visualization, and low-to-high resolution data domain transferring. We hope the release of this generative model will encourage further cooperation between all graphics, vision, and data focused professionals while demonstrating the cumulative value of every individual's complete biometric profile.