ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

ControlFoley is a new unified multimodal video-to-audio (V2A) framework designed to enhance precise control over video, text, and reference audio during audio generation. It addresses limitations in existing methods, such as weak textual controllability under visual-text conflict and imprecise stylistic control from entangled temporal and timbre information. The framework integrates CLIP with a spatio-temporal audio-visual encoder for improved alignment and textual control, and employs temporal-timbre decoupling to preserve discriminative timbre features while suppressing redundant temporal cues. ControlFoley also features a modality-robust training scheme with unified multimodal representation alignment (REPA) and random modality dropout. Additionally, the authors introduce VGGSound-TVC, a benchmark for evaluating textual controllability under visual-text conflict. Experiments show ControlFoley achieves superior controllability and strong synchronization, outperforming an industrial V2A system across various tasks.

Key takeaway

For research scientists developing video-to-audio generation systems, ControlFoley offers a robust approach to improve controllability, especially under visual-text conflict. You should consider its joint visual encoding and temporal-timbre decoupling techniques to enhance textual and stylistic control in your models. Utilizing the new VGGSound-TVC benchmark can also provide a standardized way to evaluate your system's performance in challenging cross-modal scenarios.

Key insights

ControlFoley unifies V2A generation with precise control via cross-modal conflict handling and temporal-timbre decoupling.

Principles

• Integrate CLIP for visual-text alignment
• Decouple temporal and timbre features
• Employ modality-robust training

Method

ControlFoley uses a joint visual encoding paradigm, temporal-timbre decoupling, and a modality-robust training scheme with REPA and random modality dropout for V2A generation.

In practice

• Use VGGSound-TVC for V2A textual control evaluation
• Apply joint visual encoding for better alignment
• Decouple audio features for precise style control

Topics

• ControlFoley
• Video-to-Audio Generation
• Cross-Modal Conflict
• Textual Controllability
• Temporal-Timbre Decoupling

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.