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Found 9 papers, 5 papers with code
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space
Then, we train a diffusion model in the 3D-aware latent space, thereby enabling synthesis of high-quality 3D-consistent image samples, outperforming recent state-of-the-art GAN-based methods.
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene.
ARAH: Animatable Volume Rendering of Articulated Human SDFs
We demonstrate that our proposed pipeline can generate clothed avatars with high-quality pose-dependent geometry and appearance from a sparse set of multi-view RGB videos.
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields.
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
StyleGAN in particular sets new standards for generative modeling regarding image quality and controllability.
Ranked #1 on
Image Generation
on CIFAR-10
(NFE metric)
On the Frequency Bias of Generative Models
2) Checkerboard artifacts introduced by upsampling cannot explain the spectral discrepancies alone as the generator is able to compensate for these artifacts.
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity.
Ranked #2 on
Scene Generation
on VizDoom
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain.
Multi-Scale Convolutions for Learning Context Aware Feature Representations
Finding semantic correspondences is a challenging problem.
