Learning Formation of Physically-Based Face Attributes
Ruilong Li*1,2    Karl Bladin*1    Yajie Zhao*1    Chinmay Chinara1    Owen Ingraham1   
Pengda Xiang1,2    Xinglei Ren1    Pratusha Prasad1    Bipin Kishore1    Jun Xing1    Hao Li1,2,3   
Institute for Creative Technologies1    University of Southern California2    Pinscreen3    *Equal contribution

We introduce a comprehensive framework for learning physically based face models from highly constrained facial scan data. Our deep learning based approach for 3D morphable face modeling seizes the fidelity of nearly 4000 high resolution face scans encompassing expression and identity separation (a). The model (b) combines a multitude of anatomical and physically based face attributes to generate an infinite number of digitized faces (c). Our model generates faces at pore level geometry resolution (d).
Abstract

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 the participant’s 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 individuals’ complete biometric profile.



Results

Randomly Generated Samples




Generated Identities




Identity Interpolation




Model Enhancement




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