Learning Formation of Physically-Based Face Attributes
Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, Hao Li
摘要
Identity Expression (a) (b) (c) (d) Figure 1: 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).
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