VividListener: Expressive and Controllable Listener Dynamics Modeling for Multi-Modal Responsive Interaction (2504.21718v2)

Abstract: Generating responsive listener head dynamics with nuanced emotions and expressive reactions is crucial for practical dialogue modeling in various virtual avatar animations. Previous studies mainly focus on the direct short-term production of listener behavior. They overlook the fine-grained control over motion variations and emotional intensity, especially in long-sequence modeling. Moreover, the lack of long-term and large-scale paired speaker-listener corpora including head dynamics and fine-grained multi-modality annotations (e.g., text-based expression descriptions, emotional intensity) also limits the application of dialogue modeling.Therefore, we first newly collect a large-scale multi-turn dataset of 3D dyadic conversation containing more than 1.4M valid frames for multi-modal responsive interaction, dubbed ListenerX. Additionally, we propose VividListener, a novel framework enabling fine-grained, expressive and controllable listener dynamics modeling. This framework leverages multi-modal conditions as guiding principles for fostering coherent interactions between speakers and listeners.Specifically, we design the Responsive Interaction Module (RIM) to adaptively represent the multi-modal interactive embeddings. RIM ensures the listener dynamics achieve fine-grained semantic coordination with textual descriptions and adjustments, while preserving expressive reaction with speaker behavior. Meanwhile, we design the Emotional Intensity Tags (EIT) for emotion intensity editing with multi-modal information integration, applying to both text descriptions and listener motion amplitude.Extensive experiments conducted on our newly collected ListenerX dataset demonstrate that VividListener achieves state-of-the-art performance, realizing expressive and controllable listener dynamics.

Overview of VividListener: Expressive and Controllable Listener Dynamics Modeling for Multi-Modal Responsive Interaction

The paper at hand presents "VividListener," an advanced framework focused on generating nuanced and controllable listener dynamics within multi-modal interactions. It addresses the deficits in prior research, which predominantly concentrated on short-term listener behavior production without nuanced control over motion variations or emotional intensities. The authors emphasize the need for long-sequence modeling capabilities, coupled with comprehensive datasets that contain rich annotations, which are essential for improved dialogue modeling applications.

Key Contributions

1. Dataset Creation – ListenerX: The research introduces a novel, large-scale dataset called ListenerX, which is an extensive 3D dyadic conversation dataset. This dataset encompasses over 1.4 million frames and includes detailed annotations such as textual expression descriptions and emotional intensity tags. These multilevel annotations are instrumental in fostering more precise and semantically meaningful listener dynamics modeling.
2. VividListener Framework: Central to the paper is the VividListener framework which emphasizes fine-grained listener dynamics. This is achieved through multi-modal conditioning with components like the Responsive Interaction Module (RIM) and Emotional Intensity Tags (EIT). The RIM is critical in synchronizing semantic coordination between speaker audio, motion dynamics, and listener motions, while EIT allows for adjusting the emotional intensities effectively.
3. Methodology: The framework operates via a diffusion-based generative model that leverages multi-modal conditions. By integrating adaptive representations of interactive embeddings and continuous temporal representations for listener behavioral cues, the model advances beyond existing methodologies which were constrained to basic emotional categories and short-term listener responses.

Results and Implications

The quantitative results highlight that VividListener achieves state-of-the-art performance on several metrics, ranging from Fréchet Distance (FD) indicating realism, to Shannon Index (SID) illustrating diversity and richness in motion. Enhanced cross-scenario inference demonstrates the model's robustness, providing significant improvements in the generation of coherent listener reactions adaptable to various conversational contexts.

Practically, this framework holds substantial potential for applications in human-machine interaction, robotics, and virtual avatar animations, where lifelike and emotionally responsive avatars are desirable. Theoretically, it extends the boundaries of multi-modal interaction modeling by emphasizing long-term sequence learning and control in dialogic exchanges.

Future Directions

The paper opens several avenues for future research in AI-driven dialogue systems. These include exploring further complex scenarios where both speakers and listeners are generated concurrently, thus enhancing the naturalness and interactivity of virtual agents. Additionally, incorporating advanced AI techniques for real-time emotion recognition and adaptive response generation could significantly elevate the user interaction experience.

In summary, VividListener provides a comprehensive approach to understanding listener dynamics through nuanced emotion modeling, setting a new benchmark for long-sequence interactive systems and providing a rich dataset that could support various downstream research endeavors in AI.