Eric R. Chan
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View article: Diffusion Self-Distillation for Zero-Shot Customized Image Generation
Diffusion Self-Distillation for Zero-Shot Customized Image Generation Open
Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., "identity-p…
View article: Solving Inverse Problems in Protein Space Using Diffusion-Based Priors
Solving Inverse Problems in Protein Space Using Diffusion-Based Priors Open
The interaction of a protein with its environment can be understood and controlled via its 3D structure. Experimental methods for protein structure determination, such as X-ray crystallography or cryogenic electron microscopy, shed light o…
View article: Real-Time Radiance Fields for Single-Image Portrait View Synthesis
Real-Time Radiance Fields for Single-Image Portrait View Synthesis Open
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. Given a single RGB input, our image encoder directly predicts a canonical triplane represen…
View article: Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization
Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization Open
There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for editing and animation, and robustness for single-view face re…
View article: Real-Time Radiance Fields for Single-Image Portrait View Synthesis
Real-Time Radiance Fields for Single-Image Portrait View Synthesis Open
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. Given a single RGB input, our image encoder directly predicts a canonical triplane represen…
View article: Generative Novel View Synthesis with 3D-Aware Diffusion Models
Generative Novel View Synthesis with 3D-Aware Diffusion Models Open
We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image. Our model samples from the distribution of possible renderings consistent with the input and, even in the presence of ambi…
View article: Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition
Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition Open
Capturing images is a key part of automation for high-level tasks such as scene text recognition. Low-light conditions pose a challenge for high-level perception stacks, which are often optimized on well-lit, artifact-free images. Reconstr…
View article: Generative Neural Articulated Radiance Fields
Generative Neural Articulated Radiance Fields Open
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, however, have not been demonstrated for human bodies and the…
View article: 3D GAN Inversion for Controllable Portrait Image Animation
3D GAN Inversion for Controllable Portrait Image Animation Open
Millions of images of human faces are captured every single day; but these photographs portray the likeness of an individual with a fixed pose, expression, and appearance. Portrait image animation enables the post-capture adjustment of the…
View article: Efficient Geometry-aware 3D Generative Adversarial Networks
Efficient Geometry-aware 3D Generative Adversarial Networks Open
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. Existing 3D GANs are either compute-intensive or make approximation…
View article: Acorn
Acorn Open
Neural representations have emerged as a new paradigm for applications in rendering, imaging, geometric modeling, and simulation. Compared to traditional representations such as meshes, point clouds, or volumes they can be flexibly incorpo…
View article: pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis Open
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representa…
View article: ACORN: Adaptive Coordinate Networks for Neural Scene Representation
ACORN: Adaptive Coordinate Networks for Neural Scene Representation Open
Neural representations have emerged as a new paradigm for applications in rendering, imaging, geometric modeling, and simulation. Compared to traditional representations such as meshes, point clouds, or volumes they can be flexibly incorpo…
View article: pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware\n Image Synthesis
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware\n Image Synthesis Open
We have witnessed rapid progress on 3D-aware image synthesis, leveraging\nrecent advances in generative visual models and neural rendering. Existing\napproaches however fall short in two ways: first, they may lack an underlying\n3D represe…
View article: MetaSDF: Meta-learning Signed Distance Functions
MetaSDF: Meta-learning Signed Distance Functions Open
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. Generalizing across shapes with such n…
View article: MetaSDF: Meta-Learning Signed Distance Functions
MetaSDF: Meta-Learning Signed Distance Functions Open
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. Generalizing across shapes with such n…