AVControl: Efficient Framework for Training Audio-Visual Controls
Abstract
Controlling video and audio generation requires diverse modalities, from depth and pose to camera trajectories and audio transformations, yet existing approaches either train a single monolithic model for a fixed set of controls or introduce costly architectural changes for each new modality. We introduce AVControl, a lightweight, extendable framework built on LTX-2, a joint audio-visual foundation model, where each control modality is trained as a separate LoRA on a parallel canvas that provides the reference signal as additional tokens in the attention layers, requiring no architectural changes beyond the LoRA adapters themselves. We show that simply extending image-based in-context methods to video fails for structural control, and that our parallel canvas approach resolves this. On the VACE Benchmark, we outperform all evaluated baselines on depth- and pose-guided generation, inpainting, and outpainting, and show competitive results on camera control and audio-visual benchmarks. Our framework supports a diverse set of independently trained modalities: spatially-aligned controls such as depth, pose, and edges, camera trajectory with intrinsics, sparse motion control, video editing, and, to our knowledge, the first modular audio-visual controls for a joint generation model. Our method is both compute- and data-efficient: each modality requires only a small dataset and converges within a few hundred to a few thousand training steps, a fraction of the budget of monolithic alternatives. We publicly release our code and trained LoRA checkpoints.
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 AVControl, a modular and efficient framework for training audio-visual controls that significantly improves upon existing methods by allowing for independent training of diverse modalities without extensive architectural modifications. This innovative approach not only enhances the flexibility of audio-visual models but also sets a new standard for efficiency in training processes, making it a valuable addition to the field of machine learning.
Methodology
The paper introduces AVControl, a framework that innovatively separates control modalities into lightweight LoRA adapters, allowing for efficient training without extensive architectural changes. The use of a parallel canvas to integrate reference signals into attention layers is a notable advancement, addressing limitations in existing methods that struggle with structural control in video generation. This modular approach enhances flexibility and scalability, making it easier to incorporate new modalities.
Results & evaluation
The authors conduct thorough experiments on the VACE Benchmark, demonstrating superior performance in depth- and pose-guided generation, inpainting, and outpainting compared to existing methods. The competitive results on camera control and audio-visual benchmarks further validate the effectiveness of the proposed framework. The experiments are well-structured, with clear metrics for evaluation, although more detailed comparisons with state-of-the-art methods could enhance the robustness of the findings.
Reproducibility
The authors provide public access to their code and trained LoRA checkpoints, which is a strong point for reproducibility. However, the paper could benefit from more detailed documentation on the training process and hyperparameter settings to facilitate easier replication of results by other researchers.
Limitations
The paper acknowledges some limitations, particularly in the need for a small dataset for each modality, which may not be feasible for all applications. Additionally, while the framework shows promise, its performance on more complex audio-visual tasks remains to be fully explored.
Broader impact
The AVControl framework has significant implications for the fields of audio-visual generation and machine learning, particularly in applications requiring real-time control and adaptability. Its efficient training process can democratize access to advanced audio-visual generation tools, potentially impacting industries such as entertainment, gaming, and virtual reality. The main contribution of this paper is the introduction of AVControl, a modular and efficient framework for training audio-visual controls that significantly improves upon existing methods by allowing for independent training of diverse modalities without extensive architectural modifications. This innovative approach not only enhances the flexibility of audio-visual models but also sets a new standard for efficiency in training processes, making it a valuable addition to the field of machine learning.
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