Search Results for author:
Found 6 papers, 3 papers with code
InvGAN: Invertible GANs
Our \textbf{InvGAN}, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model.
Populating 3D Scenes by Learning Human-Scene Interaction
Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art.
GIF: Generative Interpretable Faces
Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Our model has the form of a variational autoencoder, with a Gaussian mixture prior on the latent vector.
Learning Human Motion Models for Long-term Predictions
Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists.
