Deepfakes present an emerging threat in cyberspace. Recent developments in machine
learning make deepfakes highly believable, and very difficult to differentiate between what is …

In this work, we tackle the challenge of enhancing the realism and expressiveness in talking
head video generation by focusing on the dynamic and nuanced relationship between audio …

Deepfake refers to an artificial intelligence-based technique to produce manipulated videos
that look realistic. However, this good aspect of Deepfake sometimes pose serious threats to …

This paper proposes a self-supervised approach to learn universal facial representations
from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute …

Embodied scene understanding serves as the cornerstone for autonomous agents to
perceive, interpret, and respond to open driving scenarios. Such understanding is typically …

Subtle periodic signals such as blood volume pulse and respiration can be extracted from
RGB video, enabling noncontact health monitoring at low cost. Advancements in remote …

Text-driven generation models are flourishing in video generation and editing. However,
face-centric text-to-video generation remains a challenge due to the lack of a suitable …

In this paper we abstract the process of people hearing speech extracting meaningful cues
and creating various dynamically audio-consistent talking faces termed Listening and …

Large foundation models are becoming ubiquitous, but training them from scratch is
prohibitively expensive. Thus, efficiently adapting these powerful models to downstream …

Video generation remains a challenging task due to spatiotemporal complexity and the
requirement of synthesizing diverse motions with temporal consistency. Previous works …