Photorealistic Facial Texture Inference Using Deep Neural Networks
This enables high-fidelity 3D face reconstruction from single images, benefiting applications like digital avatars and historical figure restoration, but it is incremental as it builds on existing deep learning and texture synthesis methods.
The paper tackles the problem of synthesizing photorealistic texture maps for 3D face models from partial 2D views, achieving results visually comparable to state-of-the-art multi-view capture systems and validating realism through a user study.
We present a data-driven inference method that can synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild. After an initial estimation of shape and low-frequency albedo, we compute a high-frequency partial texture map, without the shading component, of the visible face area. To extract the fine appearance details from this incomplete input, we introduce a multi-scale detail analysis technique based on mid-layer feature correlations extracted from a deep convolutional neural network. We demonstrate that fitting a convex combination of feature correlations from a high-resolution face database can yield a semantically plausible facial detail description of the entire face. A complete and photorealistic texture map can then be synthesized by iteratively optimizing for the reconstructed feature correlations. Using these high-resolution textures and a commercial rendering framework, we can produce high-fidelity 3D renderings that are visually comparable to those obtained with state-of-the-art multi-view face capture systems. We demonstrate successful face reconstructions from a wide range of low resolution input images, including those of historical figures. In addition to extensive evaluations, we validate the realism of our results using a crowdsourced user study.