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Found 7 papers, 6 papers with code
AutoDecoding Latent 3D Diffusion Models
We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core.
StyleGenes: Discrete and Efficient Latent Distributions for GANs
Thus, by independently sampling a variant for each gene and combining them into the final latent vector, our approach can represent a vast number of unique latent samples from a compact set of learnable parameters.
NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions
Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors.
Arbitrary-Scale Image Synthesis
Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales.
SkyCam: A Dataset of Sky Images and their Irradiance values
Recent advances in Computer Vision and Deep Learning have enabled astonishing results in a variety of fields and applications.
AIM 2020 Challenge on Image Extreme Inpainting
This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting.
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former.
