We present a statistical, articulated 3D human shape modeling pipeline, within a fully
trainable, modular, deep learning framework. Given high-resolution complete 3D body …

Generative adversarial networks (GANs) have been extensively studied in recent years and
have been used to address several problems in the fields of image generation and computer …

While recent advances in deep neural networks have made it possible to render high-quality
images, generating photo-realistic and personalized talking head remains challenging. With …

Recent learning approaches that implicitly represent surface geometry using coordinate-
based neural representations have shown impressive results in the problem of multi-view …

We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier
morphable face models it not only captures identity-specific geometry, texture, and …

Parametric 3D models have enabled a wide variety of tasks in computer graphics and vision,
such as modeling human bodies, faces, and hands. However, the construction of these …

We propose a novel 3D morphable model for complete human heads based on hybrid
neural fields. At the core of our model lies a neural parametric representation that …

Over the last years, with the advent of Generative Adversarial Networks (GANs), many face
analysis tasks have accomplished astounding performance, with applications including, but …

In this article, we provide a detailed survey of 3D Morphable Face Models over the 20 years
since they were first proposed. The challenges in building and applying these models …

In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering
optimization pipeline, that can be used to acquire high-fidelity renderable human avatars …