Building Audio-Visual Digital Twins with Smartphones
Abstract
Digital twins today are almost entirely visual, overlooking acoustics-a core component of spatial realism and interaction. We introduce AV-Twin, the first practical system that constructs editable audio-visual digital twins using only commodity smartphones. AV-Twin combines mobile RIR capture and a visual-assisted acoustic field model to efficiently reconstruct room acoustics. It further recovers per-surface material properties through differentiable acoustic rendering, enabling users to modify materials, geometry, and layout while automatically updating both audio and visuals. Together, these capabilities establish a practical path toward fully modifiable audio-visual digital twins for real-world environments.
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 AV-Twin, a system that allows for the creation of editable audio-visual digital twins using smartphones, combining innovative acoustic modeling with user-friendly interfaces. This work represents a significant step forward in the integration of audio and visual data for realistic digital environments, with implications for multiple industries.
Methodology
The methodology presented in this paper is innovative, combining mobile room impulse response (RIR) capture with a visual-assisted acoustic field model. The use of commodity smartphones for constructing audio-visual digital twins is a significant advancement, as it democratizes access to advanced acoustic modeling techniques. The differentiable acoustic rendering for recovering surface material properties is a notable technical contribution, allowing for real-time modifications and updates to both audio and visual components. However, the paper could benefit from a more detailed explanation of the underlying algorithms and their computational efficiency.
Results & evaluation
The experimental evaluation is thorough, showcasing the effectiveness of the AV-Twin system in various scenarios. The authors provide quantitative metrics for the accuracy of acoustic reconstructions and the fidelity of the visual outputs. However, the datasets used for evaluation are not extensively described, which raises questions about the generalizability of the results. More diverse environments and material types could enhance the robustness of the findings.
Reproducibility
The paper lacks sufficient implementation details that would allow for easy reproduction of the results. While the authors mention the use of smartphones, they do not provide specifics on the hardware or software configurations used in their experiments. Additionally, the absence of a public code repository or demo URL limits the ability of other researchers to validate the findings independently.
Limitations
One limitation of the study is the reliance on commodity smartphones, which may introduce variability in the quality of the captured data. Furthermore, the system's performance may be constrained by the physical limitations of the devices used, such as microphone sensitivity and processing power. The paper also does not address potential challenges in real-world applications, such as varying environmental conditions and user expertise.
Broader impact
The potential applications of AV-Twin are vast, ranging from virtual reality environments to architectural design and acoustic engineering. By enabling users to create and modify audio-visual digital twins easily, this work could significantly enhance user interaction and experience in various fields. The approach could also inspire further research into integrating acoustics with other sensory modalities in digital twin technologies. The main contribution of this paper is the introduction of AV-Twin, a system that allows for the creation of editable audio-visual digital twins using smartphones, combining innovative acoustic modeling with user-friendly interfaces. This work represents a significant step forward in the integration of audio and visual data for realistic digital environments, with implications for multiple industries.
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