3D-Aware Image Synthesis
23 papers with code • 3 benchmarks • 4 datasets
Most implemented papers
StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
We introduce a high resolution, 3D-consistent image and shape generation technique which we call StyleSDF.
Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation
We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image.
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
In light of recent advances in NeRF-based 3D-aware generative models, we introduce a new task, Semantic-to-NeRF translation, that aims to reconstruct a 3D scene modelled by NeRF, conditioned on one single-view semantic mask as input.
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis
To address this challenge, we propose Multi-View Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware image synthesis with geometry constraints.
IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis
Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility.
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.
EpiGRAF: Rethinking training of 3D GANs
In this work, we show that it is possible to obtain a high-resolution 3D generator with SotA image quality by following a completely different route of simply training the model patch-wise.
A Survey on Deep Generative 3D-aware Image Synthesis
Recent years have seen remarkable progress in deep learning powered visual content creation.
Deep Generative Models on 3D Representations: A Survey
In this survey, we thoroughly review the ongoing developments of 3D generative models, including methods that employ 2D and 3D supervision.
Class-Continuous Conditional Generative Neural Radiance Field
Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.