WorldSonus brings real-time spatial sound to world models
A new arXiv paper introduces WorldSonus, an interactive video-to-audio framework that gives generated world-model environments synchronized sound. It uses a streaming causal autoregressive diffusion architecture that the authors report runs at a real-time factor of 0.41, plus chunk-indexed prompt scheduling so sound events can be steered mid-generation. Stereo and ambisonic supervision is used to align output stereo audio with scene geometry and camera motion.
Why it matters: World models are largely silent today, so real-time, spatially aligned audio is a step toward generated environments that work as interactive experiences.
Computer Science > Sound
arXiv:2610.08760 (cs)
[Submitted on 6 Oct 2026]
Title:WorldSonus: Bringing Sound to Worlds
Authors:Pengjun Fang, Jingyi Fa, Kam Man Wu, Jiaming Wang, Haoyuan Huang, Yaguang Wu, Xiangjun Huang, Ziyang Ma, Weijia Chen, Hongyu Liu, Zeyue Tian, Qifeng Chen
View a PDF of the paper titled WorldSonus: Bringing Sound to Worlds, by Pengjun Fang and 11 other authors
View PDF HTML (experimental)Abstract:Recent advances in world models have enabled increasingly realistic visual synthesis. However, these generated environments remain largely silent. Bringing sound to world models poses three core challenges: real-time generation to keep pace with interactive video streams, interactive control to respond to mid-stream sound instructions, and spatially aligned stereo to reflect scene geometry and camera motion. To address these demands, we introduce WorldSonus, an interactive video-to-audio framework designed for real-time spatial sound synthesis in world models. For real-time generation, WorldSonus employs a streaming causal autoregressive diffusion architecture that synthesizes audio chunks at a low real-time factor (RTF) of 0.41. For interactive control, we incorporate an audio-centric captioning pipeline with chunk-indexed prompt scheduling, enabling dynamic manipulation of sound events during generation. For spatial alignment, we leverage high-quality stereo supervision curated from diverse stereo and ambisonic data. Extensive experiments demonstrate that while tailored for world models, WorldSonus generalizes effectively to open-domain video-to-audio benchmarks, matching or outperforming state-of-the-art bidirectional models in both acoustic quality and spatial alignment. Project page: this https URL
| Comments: | 25 pages, 4 figures, 16 tables. Project page: this https URL |
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2610.08760 [cs.SD] |
| (or arXiv:2610.08760v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08760 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pengjun Fang [view email][v1] Tue, 6 Oct 2026 17:50:18 UTC (10,428 KB)
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