LiveFoley: Dynamic Real-Time Video-to-Audio Generation for Immersive Interactive Worlds

Yusheng Dai, Vladimir Iashin, Kangdi Wang, Yuxuan Jiang, Ilpo Viertola, Weiqiang Wang, Qiuhong Ke, Min Zhao, Jianfei Cai

LiveFoley: Dynamic Real-Time Video-to-Audio Generation for Immersive Interactive Worlds: 8 upvotes on Hugging Face Daily Papers, #25 of 38 papers on 2026-11-05. Day-by-day upvote history.

Upvotes over time

It had 1 upvotes by its first day, 4 upvotes by the next day, 7 upvotes by day 3, 8 upvotes by day 7, and has 8 now. It ranked #128 of 207 papers in its week and #190 of 340 in its month. Its code repository has 15 GitHub stars.

Streaming video-to-audio (V2A) generation is essential for immersive interactive applications. However, real-time constraints leave existing streaming V2A methods far behind offline models: (1) visual frontends shrink their receptive fields to cut look-ahead latency, severely degrading audio-visual alignment; (2) causal autoregressive diffusion backends denoise each audio patch in isolation, impairing audio quality. To this end, we present LiveFoley, a streaming causal autoregressive diffusion architecture that addresses both bottlenecks with imperceptible latency. For the visual frontend, we introduce a Trailing Sliding Window that converts non-causal segment-level encoders into streaming modules without retraining. By simply realigning the window, it preserves precise audio-visual alignment, while a configurable hop size reduces the frontend look-ahead well below the human perceptual threshold. Building on this tunable hop size, we propose the Unified Varhop Framework to accommodate scenarios with diverse latency and computation budgets. For the audio backend, we explore widening the denoising window of the local diffusion transformer (LocDiT) head in two temporal directions. History LocDiT adds past context to improve quality at no latency cost and serves as our default. Alternatively, Future-Warmup LocDiT breaks the autoregressive deadlock with an approximate future condition, further improving generation quality at the cost of an acceptable backend look-ahead. Extensive experiments demonstrate that LiveFoley matches offline models in temporal alignment and leads streaming systems in generation quality. Project page: https://dalision.github.io/livefoley-page/

Paper page on Hugging Face · arXiv

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