61 citations · 189 across the 17 of their papers we have counts for
17 papers
Generalizable One-shot Neural Head Avatar
Xueting Li, Shalini De Mello, Sifei Liu +3
We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image. Existing methods either involve time-consuming optimization for a specific pe…
Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization
Connor Z. Lin, Koki Nagano, Jan Kautz +5
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 ed…
Real-Time Radiance Fields for Single-Image Portrait View Synthesis
Alex Trevithick, Matthew Chan, Michael Stengel +7
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 im…
Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos
Ekta Prashnani, Koki Nagano, Shalini De Mello +2
Modern avatar generators allow anyone to synthesize photorealistic real-time talking avatars, ushering in a new era of avatar-based human communication, such as with immersive AR/V…
Generative Novel View Synthesis with 3D-Aware Diffusion Models
Eric R. Chan, Koki Nagano, Matthew A. Chan +7
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…
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
Riccardo Corvi, Davide Cozzolino, Giovanni Poggi +2
Detecting fake images is becoming a major goal of computer vision. This need is becoming more and more pressing with the continuous improvement of synthesis methods based on Genera…
RANA: Relightable Articulated Neural Avatars
Umar Iqbal, Akin Caliskan, Koki Nagano +3
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting. We only require a shor…
On the detection of synthetic images generated by diffusion models
Riccardo Corvi, Davide Cozzolino, Giada Zingarini +3
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…
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation
Yu-Ying Yeh, Koki Nagano, Sameh Khamis +3
Given a portrait image of a person and an environment map of the target lighting, portrait relighting aims to re-illuminate the person in the image as if the person appeared in an…
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
Jonathan Tremblay, Moustafa Meshry, Alex Evans +9
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 resol…
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
Amit Raj, Umar Iqbal, Koki Nagano +4
Acquisition and creation of digital human avatars is an important problem with applications to virtual telepresence, gaming, and human modeling. Most contemporary approaches for av…
Fusing Global and Local Features for Generalized AI-Synthesized Image Detection
Yan Ju, Shan Jia, Lipeng Ke +3
With the development of the Generative Adversarial Networks (GANs) and DeepFakes, AI-synthesized images are now of such high quality that humans can hardly distinguish them from re…
Efficient Geometry-aware 3D Generative Adversarial Networks
Eric R. Chan, Connor Z. Lin, Matthew A. Chan +9
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…
Frame Averaging for Equivariant Shape Space Learning
Matan Atzmon, Koki Nagano, Sanja Fidler +2
The task of shape space learning involves mapping a train set of shapes to and from a latent representation space with good generalization properties. Often, real-world collections…
Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement
Huiwen Luo, Koki Nagano, Han-Wei Kung +6
We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling pers…
One-Shot Identity-Preserving Portrait Reenactment
Sitao Xiang, Yuming Gu, Pengda Xiang +4
We present a deep learning-based framework for portrait reenactment from a single picture of a target (one-shot) and a video of a driving subject. Existing facial reenactment metho…
Photorealistic Facial Texture Inference Using Deep Neural Networks
Shunsuke Saito, Lingyu Wei, Liwen Hu +2
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. After an ini…