Recent generative models can synthesize high-quality images but often fail to generate
humans interacting with objects using their hands. This arises mostly from the model's …

In this work, we focus on generating hand grasp over objects. Compared to previous works
of generating hand poses with a given object, we aim to allow the generalization of both …

Synthesizing 3D whole-bodies that realistically grasp objects is useful for animation, mixed
reality, and robotics. This is challenging, because the hands and body need to look natural …

Understanding human intentions and actions through egocentric videos is important on the
path to embodied artificial intelligence. As a branch of egocentric vision techniques, hand …

Understanding how humans would behave during hand-object interaction is vital for
applications in service robot manipulation and extended reality. To achieve this, some …

Predicting the dynamics of interacting objects is essential for both humans and intelligent
systems. However, existing approaches are limited to simplified, toy settings and lack …

In this paper, we introduce ManiVideo, a novel method for generating consistent and
temporally coherent bimanual hand-object manipulation videos from given motion …

Grasping generation holds significant importance in both robotics and AI-generated content.
While pure network paradigms based on VAEs or GANs ensure diversity in outcomes, they …

Predicting future human behavior is an increasingly popular topic in computer vision, driven
by the interest in applications such as autonomous vehicles, digital assistants and human …

We study the problem of precisely swapping objects in videos, with a focus on those
interacted with by hands, given one user-provided reference object image. Despite the great …