3D-Aware Image Synthesis

23 papers with code • 3 benchmarks • 4 datasets

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Most implemented papers

pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

marcoamonteiro/pi-GAN CVPR 2021

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering.

Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation

hexagonprime/pix2nerf 26 Feb 2022

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.

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

autonomousvision/graf NeurIPS 2020

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.

XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors

cpeng93/XraySyn 4 Dec 2020

A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane.

CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis

PeterouZh/CIPS-3D 19 Oct 2021

The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses.

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

xingangpan/shadegan NeurIPS 2021

Motivated by the observation that a 3D object should look realistic from multiple viewpoints, these methods introduce a multi-view constraint as regularization to learn valid 3D radiance fields from 2D images.

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis

sheldontsui/gof_neurips2021 NeurIPS 2021

In this paper, we propose Generative Occupancy Fields (GOF), a novel model based on generative radiance fields that can learn compact object surfaces without impeding its training convergence.

FENeRF: Face Editing in Neural Radiance Fields

MrTornado24/FENeRF CVPR 2022

2D GANs can generate high fidelity portraits but with low view consistency.

3D-Aware Semantic-Guided Generative Model for Human Synthesis

zhangqianhui/3DSGAN 2 Dec 2021

However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications.

3D-aware Image Synthesis via Learning Structural and Textural Representations

genforce/volumegan CVPR 2022

The feature field is further accumulated into a 2D feature map as the textural representation, followed by a neural renderer for appearance synthesis.