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STEER: Steerable Dyadic Head Avatars
Kartik Teotia, Helge Rhodin, Hyeongwoo Kim +2
Facial movement and expression are central to face-to-face communication, conveying turn-taking, attention, agreement, and engagement alongside speech. While speech-driven facial a…
Polyglot: Multilingual Style Preserving Speech-Driven Facial Animation
Federico Nocentini, Kwanggyoon Seo, Qingju Liu +6
Speech-Driven Facial Animation (SDFA) has gained significant attention due to its applications in movies, video games, and virtual reality. However, most existing models are traine…
Zero-Shot Video Deraining with Video Diffusion Models
Tuomas Varanka, Juan Luis Gonzalez, Hyeongwoo Kim +2
Existing video deraining methods are often trained on paired datasets, either synthetic, which limits their ability to generalize to real-world rain, or captured by static cameras,…
Audio-Driven Universal Gaussian Head Avatars
Kartik Teotia, Helge Rhodin, Mohit Mendiratta +3
We introduce the first method for audio-driven universal photorealistic avatar synthesis, combining a person-agnostic speech model with our novel Universal Head Avatar Prior (UHAP)…
Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation
Nick Yiwen Huang, Akin Caliskan, Berkay Kicanaoglu +2
We consider the problem of disentangling 3D from large vision-language models, which we show on generative 3D portraits. This allows free-form text control of appearance attributes…
Contextual Gesture: Co-Speech Gesture Video Generation through Context-aware Gesture Representation
Pinxin Liu, Pengfei Zhang, Hyeongwoo Kim +3
Co-speech gesture generation is crucial for creating lifelike avatars and enhancing human-computer interactions by synchronizing gestures with speech. Despite recent advancements,…
KinMo: Kinematic-aware Human Motion Understanding and Generation
Pengfei Zhang, Pinxin Liu, Pablo Garrido +2
Current human motion synthesis frameworks rely on global action descriptions, creating a modality gap that limits both motion understanding and generation capabilities. A single co…
GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations
Kartik Teotia, Hyeongwoo Kim, Pablo Garrido +3
Real-time rendering of human head avatars is a cornerstone of many computer graphics applications, such as augmented reality, video games, and films, to name a few. Recent approach…
PAV: Personalized Head Avatar from Unstructured Video Collection
Akin Caliskan, Berkay Kicanaoglu, Hyeongwoo Kim
We propose PAV, Personalized Head Avatar for the synthesis of human faces under arbitrary viewpoints and facial expressions. PAV introduces a method that learns a dynamic deformabl…