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Found 22 papers, 4 papers with code
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering.
GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning
Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
A Unified Approach for Text- and Image-guided 4D Scene Generation
Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images.
Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing.
Generalizable One-shot Neural Head Avatar
We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image.
Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos
This allows us to link the synthetic video to the identity driving the expressions in the video, regardless of the facial appearance shown.
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.
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
Detecting fake images is becoming a major goal of computer vision.
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.
RANA: Relightable Articulated Neural Avatars
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting.
On the detection of synthetic images generated by diffusion models
Over the past decade, there has been tremendous progress in creating synthetic media, mainly thanks to the development of powerful methods based on generative adversarial networks (GAN).
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation
An effective approach is to supervise the training of deep neural networks with a high-fidelity dataset of desired input-output pairs, captured with a light stage.
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
We present a large-scale synthetic dataset for novel view synthesis consisting of ~300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 x 1600 pixels).
Ranked #1 on
Novel View Synthesis
on RTMV
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
In this work, we present, DRaCoN, a framework for learning full-body volumetric avatars which exploits the advantages of both the 2D and 3D neural rendering techniques.
Fusing Global and Local Features for Generalized AI-Synthesized Image Detection
Specifically, we design a two-branch model to combine global spatial information from the whole image and local informative features from multiple patches selected by a novel patch selection module.
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.
Frame Averaging for Equivariant Shape Space Learning
A natural way to incorporate symmetries in shape space learning is to ask that the mapping to the shape space (encoder) and mapping from the shape space (decoder) are equivariant to the relevant symmetries.
Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement
Cutting-edge 3D face reconstruction methods use non-linear morphable face models combined with GAN-based decoders to capture the likeness and details of a person but fail to produce neutral head models with unshaded albedo textures which is critical for creating relightable and animation-friendly avatars for integration in virtual environments.
One-Shot Identity-Preserving Portrait Reenactment
This is achieved by a novel landmark disentanglement network (LD-Net), which predicts personalized facial landmarks that combine the identity of the target with expressions and poses from a different subject.
Mesoscopic Facial Geometry Inference Using Deep Neural Networks
We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps.
Photorealistic Facial Texture Inference Using Deep Neural Networks
We present a data-driven inference method that can synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild.
