Adi Rosenthal

While recent advances in co‐speech gesture generation have achieved impressive rhythmic synchronization, synthesizing gestures that are both semantically meaningful and faithful to a speaker's unique non‐verbal style remains an open challenge. Semantic gestures, such as iconic shapes or deictic pointing, are statistically sparse, making them difficult to learn effectively within standard generative models. We present SiGnature, a framework for Stylized and Semantic Gesture generation that reconciles precise semantic control with high‐fidelity style preservation. Unlike prevalent methods that rely on entangled latent representations, SiGnature operates in an explicit joint‐rotation space. This design enables our core contribution, Joint Motion Integration (JMI), a training‐free inference mechanism capable of injecting any external motion sequence, particularly in‐the‐wild semantic gestures, directly into the diffusion process. JMI automatically identifies the specific “active joints” conveying a semantic action and injects them into the generation, while relying on the diffusion backbone to synthesize the remaining body dynamics, including posture and flow, in accordance with the pre‐learned style of the target speaker. This allows for the plug‐and‐play integration of arbitrary motions, including complex semantic gestures, without retraining or introducing the “Frankenstein” artifacts typical of cut‐and‐paste methods. Extensive experiments and perceptual studies demonstrate that SiGnature offers superior semantic motion control while maintaining smooth and natural co‐speech gesture generation and preserving the distinct characteristics of the speaker, thereby outperforming state‐of‐the‐art baselines.

In this work we extend speaker‐centric audio‐driven gesture synthesis toward a unified conversational model that jointly captures both speaking and listening behaviors. Existing speaker‐centric models effectively generate gestures aligned with speech but overlook the bidirectional dynamics that characterize natural dialogue. To address this limitation, we propose the Conversational Gesture Model (CGM), a cross‐attention‐based model capable of synthesizing gestures conditioned on interlocutor conversational cues such as gestures, tone, and textual semantics. By leveraging cross‐attention mechanisms, the model fuses interlocutor audio and text features with character gesture encodings, enabling a single system to seamlessly alternate between speaking and listening roles of the same character. Hence, our model enables a single system to act as both speaker and listener, capturing the fluid role shifts and mutual influence inherent in conversation. Experiments demonstrate that this approach preserves the quality of speaker‐driven gestures while significantly improving the realism, coherence, and responsiveness of full conversational interactions.