CMU & Meta’s AlbedoGAN Advances Realistic 3D Face Generation

In the new paper Towards Realistic Generative 3D Face Models, a research team from Carnegie Mellon University and Meta proposes a 3D controllable generative model capable of generating high-resolution textures and capturing high-frequency details in facial geometry. Their proposed AlbedoGAN outperforms state-of-the-art baselines in facial shape reconstruction.

3d face generation ai art artificial intelligence carnegie mellon carnegie mellon university deep-neural-networks face generative generative adversarial network geometry machine learning machine learning & data science meta ml paper research research team state team technology university