Representing human performance at high-fidelity is an essential building block in diverse
applications, such as film production, computer games or videoconferencing. To close the …

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 …

Abstract We present Instant Volumetric Head Avatars (INSTA), a novel approach for
reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic …

Abstract We present Vid2Avatar, a method to learn human avatars from monocular in-the-
wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos …

Recent advances in radiance fields enable photorealistic rendering of static or dynamic 3D
scenes, but still do not support explicit deformation that is used for scene manipulation or …

We propose an unsupervised method for 3D geometry-aware representation learning of
articulated objects, in which no image-pose pairs or foreground masks are used for training …

We present HandAvatar, a novel representation for hand animation and rendering, which
can generate smoothly compositional geometry and self-occlusion-aware texture …

Unsupervised learning of 3D-aware generative adversarial networks has lately made much
progress. Some recent work demonstrates promising results of learning human generative …

We introduce 3D Gaussian blendshapes for modeling photorealistic head avatars. Taking a
monocular video as input, we learn a base head model of neutral expression, along with a …

Recent advances in neural radiance fields enable novel view synthesis of photo-realistic
images in dynamic settings, which can be applied to scenarios with human animation …