Speech-driven 3D face animation aims to generate realistic facial expressions that match
the speech content and emotion. However, existing methods often neglect emotional facial …

Video recordings of speech contain correlated audio and visual information, providing a
strong signal for speech representation learning from the speaker's lip movements and the …

Speech-driven 3D facial animation is challenging due to the complex geometry of human
faces and the limited availability of 3D audio-visual data. Prior works typically focus on …

Abstract We propose DiffSHEG a Diffusion-based approach for Speech-driven Holistic 3D
Expression and Gesture generation. While previous works focused on co-speech gesture or …

In this paper, we present a video-based learning framework for animating personalized 3D
talking faces from audio. We introduce two training-time data normalizations that significantly …

We propose a method to recommend music for an input video while allowing a user to guide
music selection with free-form natural language. A key challenge of this problem setting is …

Audio-driven facial animation is a rapidly evolving field that aims to generate realistic facial
expressions and lip movements synchronized with a given audio input. This survey provides …

With the assumption that a video dataset is multimodality annotated in which auditory and
visual modalities both are labeled or class-relevant, current multimodal methods apply …

Using multiple spatial modalities has been proven helpful in improving semantic
segmentation performance. However, there are several real-world challenges that have yet …

Laughter is a unique expression, essential to affirmative social interactions of humans.
Although current 3D talking head generation methods produce convincing verbal …