ViSAGe: Video-to-Spatial Audio Generation

• URL: http://arxiv.org/abs/2506.12199v1
• Date: Fri, 13 Jun 2025 19:57:42 GMT
• Title: ViSAGe: Video-to-Spatial Audio Generation
• Authors: Jaeyeon Kim, Heeseung Yun, Gunhee Kim,
• Abstract summary: We introduce YT-Ambigen, a dataset comprising 102K 5-second YouTube video clips paired with corresponding first-order ambisonics.<n>We present Video-to-Spatial Audio Generation (ViSAGe), an end-to-end framework that generates first-order ambisonics from silent video frames.
• Score: 40.64366309719223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial audio is essential for enhancing the immersiveness of audio-visual experiences, yet its production typically demands complex recording systems and specialized expertise. In this work, we address a novel problem of generating first-order ambisonics, a widely used spatial audio format, directly from silent videos. To support this task, we introduce YT-Ambigen, a dataset comprising 102K 5-second YouTube video clips paired with corresponding first-order ambisonics. We also propose new evaluation metrics to assess the spatial aspect of generated audio based on audio energy maps and saliency metrics. Furthermore, we present Video-to-Spatial Audio Generation (ViSAGe), an end-to-end framework that generates first-order ambisonics from silent video frames by leveraging CLIP visual features, autoregressive neural audio codec modeling with both directional and visual guidance. Experimental results demonstrate that ViSAGe produces plausible and coherent first-order ambisonics, outperforming two-stage approaches consisting of video-to-audio generation and audio spatialization. Qualitative examples further illustrate that ViSAGe generates temporally aligned high-quality spatial audio that adapts to viewpoint changes.

Related papers

• SpA2V: Harnessing Spatial Auditory Cues for Audio-driven Spatially-aware Video Generation [50.03810359300705]
  SpA2V decomposes the generation process into two stages: audio-guided video planning and layout-grounded video generation.<n>We show that SpA2V excels in generating realistic videos with semantic and spatial alignment to the input audios.
  arXiv  Detail & Related papers  (2025-08-01T17:05:04Z)
• ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing [52.33281620699459]
  ThinkSound is a novel framework that leverages Chain-of-Thought (CoT) reasoning to enable stepwise, interactive audio generation and editing for videos.<n>Our approach decomposes the process into three complementary stages: semantically coherent, interactive object-centric refinement through precise user interactions, and targeted editing guided by natural language instructions.<n> Experiments demonstrate that ThinkSound achieves state-of-the-art performance in video-to-audio generation across both audio metrics and CoT metrics.
  arXiv  Detail & Related papers  (2025-06-26T16:32:06Z)
• YingSound: Video-Guided Sound Effects Generation with Multi-modal Chain-of-Thought Controls [10.429203168607147]
  YingSound is a foundation model designed for video-guided sound generation.<n>It supports high-quality audio generation in few-shot settings.<n>We show that YingSound effectively generates high-quality synchronized sounds through automated evaluations and human studies.
  arXiv  Detail & Related papers  (2024-12-12T10:55:57Z)
• Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis [28.172213291270868]
  Foley is a term commonly used in filmmaking, referring to the addition of daily sound effects to silent films or videos to enhance the auditory experience. Video-to-Audio (V2A) presents inherent challenges related to audio-visual synchronization. We construct a controllable video-to-audio model, termed Draw an Audio, which supports multiple input instructions through drawn masks and loudness signals.
  arXiv  Detail & Related papers  (2024-09-10T01:07:20Z)
• SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound [5.999777817331317]
  We introduce SEE-2-SOUND, a zero-shot approach that decomposes the task into (1) identifying visual regions of interest; (2) locating these elements in 3D space; (3) generating mono-audio for each; and (4) integrating them into spatial audio. Using our framework, we demonstrate compelling results for generating spatial audio for high-quality videos, images, and dynamic images from the internet, as well as media generated by learned approaches.
  arXiv  Detail & Related papers  (2024-06-06T22:55:01Z)
• V2Meow: Meowing to the Visual Beat via Video-to-Music Generation [47.076283429992664]
  V2Meow is a video-to-music generation system capable of producing high-quality music audio for a diverse range of video input types. It synthesizes high-fidelity music audio waveforms solely by conditioning on pre-trained general-purpose visual features extracted from video frames.
  arXiv  Detail & Related papers  (2023-05-11T06:26:41Z)
• AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis [61.07542274267568]
  We study a new task -- real-world audio-visual scene synthesis -- and a first-of-its-kind NeRF-based approach for multimodal learning. We propose an acoustic-aware audio generation module that integrates prior knowledge of audio propagation into NeRF. We present a coordinate transformation module that expresses a view direction relative to the sound source, enabling the model to learn sound source-centric acoustic fields.
  arXiv  Detail & Related papers  (2023-02-04T04:17:19Z)
• Geometry-Aware Multi-Task Learning for Binaural Audio Generation from Video [94.42811508809994]
  We propose an audio spatialization method that draws on visual information in videos to convert their monaural (single-channel) audio to audio. Whereas existing approaches leverage visual features extracted directly from video frames, our approach explicitly disentangles the geometric cues present in the visual stream to guide the learning process.
  arXiv  Detail & Related papers  (2021-11-21T19:26:45Z)
• Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation [45.526051369551915]
  We propose an audio spatialization framework to convert a monaural video into a one exploiting the relationship across audio and visual components. Experiments on benchmark datasets confirm the effectiveness of our proposed framework in both semi-supervised and fully supervised scenarios.
  arXiv  Detail & Related papers  (2021-05-03T09:34:11Z)
• Lets Play Music: Audio-driven Performance Video Generation [58.77609661515749]
  We propose a new task named Audio-driven Per-formance Video Generation (APVG) APVG aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip.
  arXiv  Detail & Related papers  (2020-11-05T03:13:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.