Dyadic Interaction Modeling for Social Behavior Generation: A Framework for Realistic Facial Expressions in Conversations

Core Concepts

Effective framework for generating realistic listener motions through Dyadic Interaction Modeling.

Abstract

Human-human communication involves complex interactions, requiring models to capture dyadic context. The proposed framework, DIM, utilizes pre-training and contrastive learning to generate lifelike facial expressions and head motions. Extensive experiments show superior performance in listener motion generation, establishing a new state-of-the-art. The approach addresses limitations of existing methods by modeling bidirectional interactions and enhancing diversity in generated motions. DIM-Listener and DIM-Speaker demonstrate the ability to create realistic behaviors from audio-visual inputs.

Stats

CANDOR dataset consists of 1,656 conversations in English. ViCo dataset includes 483 video sequences with 50 unique listeners. LM_Listener dataset contains 2366 training segments with a single listener (Trevor Noah).

Quotes

"We present an effective framework for creating 3D facial motions in dyadic interactions." "Our method not only generates listener behaviors from speaker audio-visual inputs but could also adeptly produce speaker facial motions." "Extensive experiments demonstrate the superiority of our framework in generating listener motions."

Key Insights Distilled From

Dyadic Interaction Modeling for Social Behavior Generation

by Minh Tran,Di... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09069.pdf

Deeper Inquiries

How can the proposed Dyadic Interaction Modeling be applied to other domains beyond human-computer interaction?

Dyadic Interaction Modeling, as proposed in the context of social behavior generation, has applications beyond just human-computer interaction. One potential application is in virtual reality (VR) environments where realistic interactions between avatars are crucial for creating immersive experiences. By incorporating Dyadic Interaction Modeling into VR systems, developers can enhance the realism of avatar interactions and make virtual environments more engaging for users. Another domain where this modeling approach could be beneficial is in media forensics. In scenarios where analyzing and reconstructing social interactions from video footage is necessary, such as in criminal investigations or surveillance, Dyadic Interaction Modeling can help generate more accurate representations of these interactions. This could aid forensic experts in understanding the dynamics between individuals captured on video and extracting valuable information from these recordings. Furthermore, industries like entertainment and gaming could leverage Dyadic Interaction Modeling to create lifelike characters with dynamic behaviors that respond realistically to different stimuli. By integrating this modeling technique into character animation pipelines, animators can produce more authentic performances that resonate with audiences.

How might advancements in virtual reality and media forensics benefit from the capabilities of Dyadic Interaction Modeling?

Advancements in virtual reality (VR) stand to benefit significantly from the capabilities of Dyadic Interaction Modeling. In VR applications, such as training simulations or educational experiences, realistic interpersonal interactions play a vital role in enhancing user engagement and learning outcomes. By implementing Dyadic Interaction Modeling techniques, VR developers can create more sophisticated AI-driven characters that respond dynamically to user input and mimic natural conversational behaviors. In the realm of media forensics, where analyzing audio-visual data for investigative purposes is critical, Dyadic Interaction Modeling offers a powerful tool for reconstructing social interactions accurately. This technology can assist forensic analysts in deciphering complex relationships between individuals captured on camera by generating detailed models of their nonverbal cues and behaviors during conversations or events. Overall, advancements in both VR and media forensics will benefit from leveraging Dyadic Interaction Modeling to improve the authenticity and realism of simulated interactions within virtual environments while also aiding investigators in interpreting social dynamics depicted in multimedia recordings effectively.