We tackle the task of reconstructing hand-object interactions from short video clips. Given an
input video, our approach casts 3D inference as a per-video optimization and recovers a …

What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

Previous works concerning single-view hand-held object reconstruction typically rely on
supervision from 3D ground-truth models which are hard to collect in real world. In contrast …

The research topic of estimating hand pose from the images of hand-object interaction has
the potential for replicating natural hand behavior in many practical applications of virtual …

We propose G-HOP a denoising diffusion based generative prior for hand-object
interactions that allows modeling both the 3D object and a human hand conditioned on the …

Human hands are highly articulated and versatile at handling objects. Jointly estimating the
3D poses of a hand and the object it manipulates from a monocular camera is challenging …

This work proposes a novel learning framework for visual hand dynamics analysis that takes
into account the physiological aspects of hand motion. The existing models which are …

Recent works in hand-object reconstruction mainly focus on the single-view and dense multi-
view settings. On the one hand single-view methods can leverage learned shape priors to …

Reconstructing hand-held objects from a single RGB image without known 3D object
templates, category prior, or depth information is a vital yet challenging problem in computer …

We interact with the world with our hands and see it through our own (egocentric)
perspective. A holistic 3D understanding of such interactions from egocentric views is …