Audio-Visual World Models: Towards Multisensory Imagination in Sight and Sound
Abstract
World models simulate environmental dynamics to enable agents to plan and reason about future states. While existing approaches have primarily focused on visual observations, real-world perception inherently involves multiple sensory modalities. Audio provides crucial spatial and temporal cues such as sound source localization and acoustic scene properties, yet its integration into world models remains largely unexplored. No prior work has formally defined what constitutes an audio-visual world model or how to jointly capture binaural spatial audio and visual dynamics under precise action control with task reward prediction. This work presents the first formal framework for Audio-Visual World Models (AVWM), formulating multimodal environment simulation as a partially observable Markov decision process with synchronized audio-visual observations, fine-grained actions, and task rewards. To address the lack of suitable training data, we construct AVW-4k, a dataset comprising 30 hours of binaural audio-visual trajectories with action annotations and reward signals across 76 indoor environments. We propose AV-CDiT, an Audio-Visual Conditional Diffusion Transformer with a novel modality expert architecture that balances visual and auditory learning, optimized through a three-stage training strategy for effective multimodal integration. Extensive experiments demonstrate that AV-CDiT achieves high-fidelity multimodal prediction across visual and auditory modalities with reward. Furthermore, we validate its practical utility in continuous audio-visual navigation tasks, where AVWM significantly enhances the agent's performance.
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 a formal framework for Audio-Visual World Models that integrates audio and visual modalities for enhanced environmental understanding and planning in AI agents. This work significantly advances the field by addressing the limitations of existing unimodal models and providing a comprehensive methodology for multimodal integration.
Methodology
The paper introduces a novel framework for Audio-Visual World Models (AVWM) that integrates audio and visual modalities into a unified partially observable Markov decision process (POMDP). The proposed AV-CDiT architecture employs a modality expert design and a three-stage training strategy to balance the learning of visual and auditory information. This approach is innovative as it addresses the existing gap in multimodal world modeling, which has predominantly focused on visual data. The methodology is well-structured and demonstrates a clear progression from problem formulation to model implementation.
Results & evaluation
The authors conduct extensive experiments using the newly constructed AVW-4k dataset, which includes 30 hours of synchronized audio-visual data with action annotations and reward signals. The results show that AV-CDiT achieves high-fidelity predictions in both visual and auditory modalities, validating its effectiveness in continuous audio-visual navigation tasks. The evaluation metrics used are appropriate, covering perceptual similarity, audio realism, and reward prediction, which provide a comprehensive assessment of the model's performance.
Reproducibility
The paper provides detailed implementation details, including the architecture of AV-CDiT, the training strategy, and the dataset construction process. However, the lack of a publicly available code repository or demo URL limits the reproducibility of the results, as other researchers cannot easily replicate the experiments or utilize the model.
Limitations
One limitation of the study is the reliance on a simulated environment for data collection, which may not fully capture the complexities of real-world audio-visual interactions. Additionally, while the model shows promise in navigation tasks, its performance in more diverse and complex environments remains to be evaluated. The paper also does not address potential biases in the dataset or the implications of training on a limited set of indoor environments.
Broader impact
The development of AVWMs has significant implications for advancing embodied AI systems, particularly in applications requiring multimodal perception and decision-making, such as robotics, autonomous vehicles, and augmented reality. By enabling agents to reason about their environment using both visual and auditory information, this research could enhance the capabilities of AI in real-world scenarios, leading to more intelligent and adaptable systems. The main contribution of this paper is the introduction of a formal framework for Audio-Visual World Models that integrates audio and visual modalities for enhanced environmental understanding and planning in AI agents. This work significantly advances the field by addressing the limitations of existing unimodal models and providing a comprehensive methodology for multimodal integration.
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