Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation

TL;DR

Unison is a unified framework that improves the coherence and synchronization of motion, speech, and sound in human-centric videos by explicitly modeling their interactions.

Contribution

It introduces novel semantic-guided harmonization and bidirectional cross-modal forcing strategies for enhanced multimodal alignment.

Findings

Achieves state-of-the-art audio perceptual quality.

Improves cross-modal synchronization accuracy.

Effectively mitigates speech dominance and environmental noise.

Abstract

Motion, speech, and sound effects are fundamental elements of human-centric videos, yet their heterogeneous temporal characteristics make joint generation highly challenging. Existing audio-video generation models often fail to maintain consistent alignment across these modalities, leading to noticeable mismatches between motion, speech, and environmental sounds. We present Unison, a unified framework that explicitly promotes coherence across the motion, speech, and sound modalities. Within the audio stream, Unison employs a semantic-guided harmonization strategy that decouples the generation of speech and sound-effect components. Leveraging bidirectional audio cross-attention and semantic-conditioned gating for semantic-driven adaptive recomposition, this approach effectively mitigates speech dominance and enhances acoustic clarity. For audio-motion synchronization, we propose a bidirectional cross-modal forcing strategy where the cleaner modality guides the noisier one through decoupled denoising schedules, reinforced by a progressive stabilization strategy. Extensive experiments demonstrate that Unison achieves state-of-the-art performance in both audio perceptual quality and cross-modal synchronization, highlighting the importance of explicit multimodal harmonization in human-centric video generation.