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Found 10 papers, 4 papers with code
Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization
To tackle this problem, we propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing.
Real-Time Radiance Fields for Single-Image Portrait View Synthesis
We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e. g., face portrait) in real-time.
Generative Novel View Synthesis with 3D-Aware Diffusion Models
We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image.
Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition
Capturing images is a key part of automation for high-level tasks such as scene text recognition.
Generative Neural Articulated Radiance Fields
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress.
3D GAN Inversion for Controllable Portrait Image Animation
Portrait image animation enables the post-capture adjustment of these attributes from a single image while maintaining a photorealistic reconstruction of the subject's likeness or identity.
Efficient Geometry-aware 3D Generative Adversarial Networks
Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge.
ACORN: Adaptive Coordinate Networks for Neural Scene Representation
Here, we introduce a new hybrid implicit-explicit network architecture and training strategy that adaptively allocates resources during training and inference based on the local complexity of a signal of interest.
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering.
Ranked #3 on
Scene Generation
on VizDoom
MetaSDF: Meta-learning Signed Distance Functions
Neural implicit shape representations are an emerging paradigm that offers many potential benefits over conventional discrete representations, including memory efficiency at a high spatial resolution.
