Generating realistic 3D faces is of high importance for computer graphics and computer
vision applications. Generally, research on 3D face generation revolves around linear …

3D facial avatar reconstruction has been a significant research topic in computer graphics
and computer vision, where photo-realistic rendering and flexible controls over poses and …

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 …

Synthesis and reconstruction of 3D human head has gained increasing interests in
computer vision and computer graphics recently. Existing state-of-the-art 3D generative …

Over the past few years, Generative Adversarial Networks (GANs) have garnered increased
interest among researchers in Computer Vision, with applications including, but not limited …

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East
Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan …

Generative Adversarial Networks (GANs) are currently the method of choice for generating
visual data. Certain GAN architectures and training methods have demonstrated exceptional …

We present a framework for training GANs with explicit control over generated facial images.
We are able to control the generated image by settings exact attributes such as age, pose …

Recent advances in 3D-aware GAN models have enabled the generation of realistic and
controllable human body images. However, existing methods focus on the control of major …

The problem of modeling an animatable 3D human head avatar under lightweight setups is
of significant importance but has not been well solved. Existing 3D representations either …