Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

We present an approach that can reconstruct hands in 3D from monocular input. Our
approach for Hand Mesh Recovery HaMeR follows a fully transformer-based architecture …

We present InterHandGen a novel framework that learns the generative prior of two-hand
interaction. Sampling from our model yields plausible and diverse two-hand shapes in close …

Recent 4D shape representations model continuous temporal evolution of implicit shapes by
(1) learning query flows without leveraging shape and articulation priors or (2) decoding …

Hand pose estimation from a single image has many applications. However, approaches to
full 3D body pose estimation are typically trained on day-to-day activities or actions. As such …

Hand tracking is relevant to such a variety of applications including human-robot interaction
(HRI), human-computer interaction (HCI), virtual reality (VR), and augmented reality (AR) …

We introduce HuTuMotion, an innovative approach for generating natural human motions
that navigates latent motion diffusion models by leveraging few-shot human feedback …

Creating personalized hand avatars is important to offer a realistic experience to users on
AR/VR platforms. While most prior studies focused on reconstructing 3D hand shapes some …

Contrastive Language-Image Pre-training (CLIP) starts to emerge in many computer vision
tasks and has achieved promising performance. However, it remains underexplored …

In this paper, we present SignAvatars, the first large-scale multi-prompt 3D sign language
(SL) motion dataset designed to bridge the communication gap for hearing-impaired …