Language-Guided Joint Audio-Visual Editing via One-Shot Adaptation

• URL: http://arxiv.org/abs/2410.07463v1
• Date: Wed, 9 Oct 2024 22:02:30 GMT
• Title: Language-Guided Joint Audio-Visual Editing via One-Shot Adaptation
• Authors: Susan Liang, Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu,
• Abstract summary: We introduce a novel task called language-guided joint audio-visual editing. Given an audio and image pair of a sounding event, this task aims at generating new audio-visual content by editing the given sounding event conditioned on the language guidance. We propose a new diffusion-based framework for joint audio-visual editing and introduce two key ideas.
• Score: 56.92841782969847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a novel task called language-guided joint audio-visual editing. Given an audio and image pair of a sounding event, this task aims at generating new audio-visual content by editing the given sounding event conditioned on the language guidance. For instance, we can alter the background environment of a sounding object while keeping its appearance unchanged, or we can add new sounds contextualized to the visual content. To address this task, we propose a new diffusion-based framework for joint audio-visual editing and introduce two key ideas. Firstly, we propose a one-shot adaptation approach to tailor generative diffusion models for audio-visual content editing. With as few as one audio-visual sample, we jointly transfer the audio and vision diffusion models to the target domain. After fine-tuning, our model enables consistent generation of this audio-visual sample. Secondly, we introduce a cross-modal semantic enhancement approach. We observe that when using language as content editing guidance, the vision branch may overlook editing requirements. This phenomenon, termed catastrophic neglect, hampers audio-visual alignment during content editing. We therefore enhance semantic consistency between language and vision to mitigate this issue. Extensive experiments validate the effectiveness of our method in language-based audio-visual editing and highlight its superiority over several baseline approaches. We recommend that readers visit our project page for more details: https://liangsusan-git.github.io/project/avedit/.

Related papers

• Exploring the Role of Audio in Video Captioning [59.679122191706426]
  We present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. We propose new local-global fusion mechanisms to improve information exchange across audio and video.
  arXiv  Detail & Related papers  (2023-06-21T20:54:52Z)
• Align, Adapt and Inject: Sound-guided Unified Image Generation [50.34667929051005]
  We propose a unified framework 'Align, Adapt, and Inject' (AAI) for sound-guided image generation, editing, and stylization. Our method adapts input sound into a sound token, like an ordinary word, which can plug and play with existing Text-to-Image (T2I) models. Our proposed AAI outperforms other text and sound-guided state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-20T12:50:49Z)
• DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment [30.38594416942543]
  We propose a novel and personalized text-to-sound generation approach with visual alignment based on latent diffusion models, namely DiffAVA. Our DiffAVA leverages a multi-head attention transformer to aggregate temporal information from video features, and a dual multi-modal residual network to fuse temporal visual representations with text embeddings. Experimental results on the AudioCaps dataset demonstrate that the proposed DiffAVA can achieve competitive performance on visual-aligned text-to-audio generation.
  arXiv  Detail & Related papers  (2023-05-22T10:37:27Z)
• Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment [22.912401512161132]
  We design a model that works by scheduling the learning procedure of each model component to associate audio-visual modalities. We translate the input audio to visual features, then use a pre-trained generator to produce an image. We obtain substantially better results on the VEGAS and VGGSound datasets than prior approaches.
  arXiv  Detail & Related papers  (2023-03-30T16:01:50Z)
• Language-Guided Audio-Visual Source Separation via Trimodal Consistency [64.0580750128049]
  A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform. We adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets.
  arXiv  Detail & Related papers  (2023-03-28T22:45:40Z)
• Fine-grained Audible Video Description [61.81122862375985]
  We construct the first fine-grained audible video description benchmark (FAVDBench) For each video clip, we first provide a one-sentence summary of the video, followed by 4-6 sentences describing the visual details and 1-2 audio-related descriptions at the end. We demonstrate that employing fine-grained video descriptions can create more intricate videos than using captions.
  arXiv  Detail & Related papers  (2023-03-27T22:03:48Z)
• Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention [54.4258176885084]
  How to accurately recognize ambiguous sounds is a major challenge for audio captioning. We propose visually-aware audio captioning, which makes use of visual information to help the description of ambiguous sounding objects. Our proposed method achieves state-of-the-art results on machine translation metrics.
  arXiv  Detail & Related papers  (2022-10-28T22:45:41Z)

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.