| CPC G06T 13/80 (2013.01) [G06T 3/4053 (2013.01); G06T 19/20 (2013.01); G06T 2219/2024 (2013.01)] | 20 Claims |
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1. A computer-implemented method comprising:
accessing a first set of machine learning models trained to generate, via a latent variable space, a three-dimensional (3D) mesh and a plurality of two-dimensional (2D) texture maps corresponding to the three-dimensional mesh;
obtaining a first set of input information configured to generate a first facial model having a first identity;
generating, by the first set of machine learning models, the first facial model from the first set of input information, wherein the first facial model includes a plurality of 2D texture maps and a 3D facial mesh of a first face;
accessing a second machine learning model trained to generate photorealistic 2D images based on an input image; and
generating, using the second machine learning model, at least one target photorealistic 2D image based on the first facial model;
accessing a differentiable rendering engine, wherein the differentiable rendering engine is configured to modify facial models based on target 2D images;
modifying, by the differentiable rendering engine, the first facial model based on the at least one target photorealistic 2D image; and
outputting, by the differentiable rendering engine, an enhanced first facial model, wherein the enhanced first facial model includes a modified version of the plurality of 2D texture maps and a modified version of the 3D facial mesh.
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