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arXiv · 2505.12734

SounDiT: Geo-Contextual Soundscape-to-Landscape Generation

Junbo Wang·Haofeng Tan·Bowen Liao·Albert Jiang·Teng Fei·Qixing Huang·Bing Zhou·Zhengzhong Tu·Shan Ye·Yuhao Kang
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Abstract

Recent audio-to-image models have shown impressive performance in generating images of specific objects conditioned on their corresponding sounds. However, these models fail to reconstruct real-world landscapes conditioned on environmental soundscapes. To address this gap, we present Geo-contextual Soundscape-to-Landscape (GeoS2L) generation, a novel and practically significant task that aims to synthesize geographically realistic landscape images from environmental soundscapes. To support this task, we construct two large-scale geo-contextual multi-modal datasets, SoundingSVI and SonicUrban, which pair diverse environmental soundscapes with real-world landscape images. We propose SounDiT, a diffusion transformer (DiT)-based model that incorporates environmental soundscapes and geo-contextual scene conditioning to synthesize geographically coherent landscape images. Furthermore, we propose the Place Similarity Score (PSS), a practically-informed geo-contextual evaluation framework to measure consistency between input soundscapes and generated landscape images. Extensive experiments demonstrate that SounDiT outperforms existing baselines in the GeoS2L, while the PSS effectively captures multi-level generation consistency across element, scene,and human perception. Project page: https://gisense.github.io/SounDiT-Page/

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BibTeXRIS

Junbo Wang, Haofeng Tan, Bowen Liao, Albert Jiang, Teng Fei, Qixing Huang, Bing Zhou, Zhengzhong Tu, Shan Ye, Yuhao Kang. 2025-05-19. SounDiT: Geo-Contextual Soundscape-to-Landscape Generation. https://arxiv.org/abs/2505.12734

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