MMControl: Unified Multi-Modal Control for Joint Audio-Video Generation
Liyang Li, Wen Wang, Canyu Zhao, Tianjian Feng, Zhiyue Zhao, Hao Chen, and Chunhua Shen

TL;DR
MMControl introduces a unified framework for multi-modal control in joint audio-video generation, enabling fine-grained, composable control over multiple conditions including visual and acoustic signals.
Contribution
It presents a dual-stream conditional injection mechanism and modality-specific guidance scaling for enhanced multi-modal controllability in diffusion-based models.
Findings
Achieves identity and timbre consistency in generated videos and audio.
Enables independent, dynamic adjustment of control signals during inference.
Demonstrates fine-grained, composable control over multiple generation aspects.
Abstract
Recent advances in Diffusion Transformers (DiTs) have enabled high-quality joint audio-video generation, producing videos with synchronized audio within a single model. However, existing controllable generation frameworks are typically restricted to video-only control. This restricts comprehensive controllability and often leads to suboptimal cross-modal alignment. To bridge this gap, we present MMControl, which enables users to perform Multi-Modal Control in joint audio-video generation. MMControl introduces a dual-stream conditional injection mechanism. It incorporates both visual and acoustic control signals, including reference images, reference audio, depth maps, and pose sequences, into a joint generation process. These conditions are injected through bypass branches into a joint audio-video Diffusion Transformer, enabling the model to simultaneously generate identity-consistent…
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