“High-Fidelity Facial Reconstruction From a Single Photo Using Photo-Realistic Rendering” by Dias, Roche, Fernandes and Orvalho


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  • ©Mariana Dias, Alexis Roche, Margarida Fernandes, and Verónica Orvalho

Conference:


  • SIGGRAPH 2022
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Type(s):


Title:


    High-Fidelity Facial Reconstruction From a Single Photo Using Photo-Realistic Rendering

Program Title:


    Labs Demo

Presenter(s):


Description:


    We propose a fully automated method for realistic 3D face reconstruction from a single frontal photo that produces a high-resolution head mesh and a diffuse map. The photo is input to a convolutional neural network that estimates the weights of a morphable model to produce an initial head shape that is further adjusted through landmark-guided deformation. Two key features of the method are: 1) the network is exclusively trained on synthetic photos that are photo-realistic enough to learn real shape predictive features, making it unnecessary to train with real facial photos and corresponding 3D scans; 2) the landmarking statistical errors are incorporated in the reconstruction for optimal accuracy. While the method is based on a limited amount of real data, we show that it robustly and quickly performs plausible face reconstructions from real photos.

References:


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ACM Digital Library Publication:


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