Foley Control: Aligning a Frozen Latent Text-to-Audio Model to Video
Abstract
Foley Control is a lightweight approach to video-guided Foley that keeps pretrained single-modality models frozen and learns only a small cross-attention bridge between them. We connect V-JEPA2 video embeddings to a frozen Stable Audio Open DiT text-to-audio (T2A) model by inserting compact video cross-attention after the model's existing text cross-attention, so prompts set global semantics while video refines timing and local dynamics. The frozen backbones retain strong marginals (video; audio given text) and the bridge learns the audio-video dependency needed for synchronization -- without retraining the audio prior. To cut memory and stabilize training, we pool video tokens before conditioning. On curated video-audio benchmarks, Foley Control delivers competitive temporal and semantic alignment with far fewer trainable parameters than recent multi-modal systems, while preserving prompt-driven controllability and production-friendly modularity (swap/upgrade encoders or the T2A backbone without end-to-end retraining). Although we focus on Video-to-Foley, the same bridge design can potentially extend to other audio modalities (e.g., speech).
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Foley Control, a novel framework that effectively integrates video guidance into a frozen text-to-audio generator, achieving competitive performance with significantly reduced computational requirements. This work represents a meaningful advancement in the field of multimodal machine learning, particularly in the context of audio-visual synchronization tasks.
Methodology
The methodology presented in this paper is innovative in its approach to leveraging a frozen text-to-audio model while introducing a lightweight cross-attention bridge to integrate video embeddings. This design choice allows for the retention of the generative capabilities of the audio model while enabling temporal and semantic alignment with video inputs. The use of pooling strategies for video tokens to reduce computational costs without sacrificing performance is a noteworthy aspect of the methodology. The integration of rotary position embeddings (RoPE) for improved temporal grounding is also a clever enhancement that addresses synchronization challenges effectively.
Results & evaluation
The experimental evaluation is robust, utilizing a curated dataset derived from Kinetics-700, which is appropriate given the focus on video-to-audio generation. The paper includes comprehensive comparisons with state-of-the-art methods, demonstrating the effectiveness of the proposed approach in terms of both temporal and semantic alignment. The results indicate that Foley Control achieves competitive performance while significantly reducing the number of trainable parameters, highlighting its efficiency. The use of various metrics to assess performance adds rigor to the evaluation.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including the training procedures, dataset curation, and evaluation metrics. However, the lack of a publicly available code repository or demo limits the reproducibility of the results. Future work should consider making the implementation accessible to facilitate validation and further research.
Limitations
The paper acknowledges some limitations, such as the potential loss of fine-grained spatial cues due to pooling and the assumption of clean input data. Additionally, the method does not explicitly model complex audio scenarios like spatial acoustics or real-time alignment, which could be critical in practical applications. These limitations suggest areas for future research and improvement.
Broader impact
The proposed method has significant implications for the fields of sound design in film, gaming, and virtual reality, where high-quality audio synchronization with video is crucial. The lightweight nature of the approach makes it suitable for production environments where computational resources are limited. Furthermore, the modularity of the framework allows for future enhancements and adaptations to other audio modalities, broadening its applicability. The main contribution of this paper is the introduction of Foley Control, a novel framework that effectively integrates video guidance into a frozen text-to-audio generator, achieving competitive performance with significantly reduced computational requirements. This work represents a meaningful advancement in the field of multimodal machine learning, particularly in the context of audio-visual synchronization tasks.
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