Search Results for author:
Found 5 papers, 2 papers with code
AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs
Nevertheless, to the best of our knowledge, there is no method which can produce render-ready high-resolution 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data.
Deep Polarization Imaging for 3D shape and SVBRDF Acquisition
We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues.
AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"
Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image.
Acquiring Axially-Symmetric Transparent Objects Using Single-View Transmission Imaging
We propose a novel, practical solution for high quality reconstruction of axially-symmetric transparent objects.
Active Preference Learning with Discrete Choice Data
We propose an active learning algorithm that learns a continuous valuation model from discrete preferences.
