MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models

TL;DR

MambaTalk introduces an efficient gesture synthesis method using state space models and multimodal integration, achieving high diversity and quality in generated gestures with lower computational costs.

Contribution

The paper presents MambaTalk, a novel two-stage SSM-based framework that significantly improves gesture diversity and realism while reducing computational complexity.

Findings

Matches or exceeds state-of-the-art performance

Enhances gesture diversity and rhythm

Reduces computational complexity

Abstract

Gesture synthesis is a vital realm of human-computer interaction, with wide-ranging applications across various fields like film, robotics, and virtual reality. Recent advancements have utilized the diffusion model and attention mechanisms to improve gesture synthesis. However, due to the high computational complexity of these techniques, generating long and diverse sequences with low latency remains a challenge. We explore the potential of state space models (SSMs) to address the challenge, implementing a two-stage modeling strategy with discrete motion priors to enhance the quality of gestures. Leveraging the foundational Mamba block, we introduce MambaTalk, enhancing gesture diversity and rhythm through multimodal integration. Extensive experiments demonstrate that our method matches or exceeds the performance of state-of-the-art models. Our project is publicly available at https://kkakkkka.github.io/MambaTalk