Tag: Generative Adversarial Network

AI Machine Learning & Data Science Research

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.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Princeton U & Adobe’s 3D-FM GAN Enables Precise 3D-Controllable Face Manipulation

In the new paper 3D-FM GAN: Towards 3D-Controllable Face Manipulation, a team from Princeton University and Adobe Research presents 3D-FM GAN, a novel conditional GAN framework that enables precise 3D-controllable face manipulation with high photorealism and strong identity preservation without requiring any manual tuning or optimizations.

AI Machine Learning & Data Science Research

NVIDIA’s StyleGAN3 Is Fully Equivariant to Translation and Rotation, Improving GAN-Based Animation Generation

A NVIDIA and Aalto University research team presents StyleGAN3, a novel generative adversarial network (GAN) architecture where the exact sub-pixel position of each feature is exclusively inherited from the underlying coarse features, enabling a more natural transformation hierarchy and advancing GAN-based animation generation.


PixelGAN Autoencoders

PixelGAN is an autoencoder for which the generative path is a convolutional autoregressive neural network on pixels, conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.