Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

• URL: http://arxiv.org/abs/2412.15322v1
• Date: Thu, 19 Dec 2024 18:59:55 GMT
• Title: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
• Authors: Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, Yuki Mitsufuji,
• Abstract summary: We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples. MMAudio achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance.
• Score: 56.01110988816489
• License:
• Abstract: We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples. Additionally, we improve audio-visual synchrony with a conditional synchronization module that aligns video conditions with audio latents at the frame level. Trained with a flow matching objective, MMAudio achieves new video-to-audio state-of-the-art among public models in terms of audio quality, semantic alignment, and audio-visual synchronization, while having a low inference time (1.23s to generate an 8s clip) and just 157M parameters. MMAudio also achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance. Code and demo are available at: https://hkchengrex.github.io/MMAudio

Related papers

• 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. It supports high-quality audio generation in few-shot settings. 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)
• Audio-Agent: Leveraging LLMs For Audio Generation, Editing and Composition [72.22243595269389]
  We introduce Audio-Agent, a framework for audio generation, editing and composition based on text or video inputs. In our method, we utilize a pre-trained TTA diffusion network as the audio generation agent to work in tandem with GPT-4. For video-to-audio (VTA) tasks, most existing methods require training a timestamp detector to synchronize video events with the generated audio.
  arXiv  Detail & Related papers  (2024-10-04T11:40:53Z)
• Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Sound [6.638504164134713]
  Foley sound synthesis is crucial for multimedia production, enhancing user experience by synchronizing audio and video both temporally and semantically. Recent studies on automating this labor-intensive process through video-to-sound generation face significant challenges. We propose Video-Foley, a video-to-sound system using Root Mean Square (RMS) as a temporal event condition with semantic timbre prompts.
  arXiv  Detail & Related papers  (2024-08-21T18:06:15Z)
• Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity [12.848371604063168]
  We propose a V2A generative model, named MaskVAT, that interconnects a full-band high-quality general audio with a sequence-to-sequence masked generative model. Our results show that, by combining a high-quality with the proper pre-trained audio-visual features and a sequence-to-sequence parallel structure, we are able to yield highly synchronized results.
  arXiv  Detail & Related papers  (2024-07-15T01:49:59Z)
• Read, Watch and Scream! Sound Generation from Text and Video [23.990569918960315]
  Video serves as a conditional control for a text-to-audio generation model. We employ a well-performing text-to-audio model to consolidate the video control. Our method shows superiority in terms of quality, controllability, and training efficiency.
  arXiv  Detail & Related papers  (2024-07-08T01:59:17Z)
• Large-scale unsupervised audio pre-training for video-to-speech synthesis [64.86087257004883]
  Video-to-speech synthesis is the task of reconstructing the speech signal from a silent video of a speaker. In this paper we propose to train encoder-decoder models on more than 3,500 hours of audio data at 24kHz. We then use the pre-trained decoders to initialize the audio decoders for the video-to-speech synthesis task.
  arXiv  Detail & Related papers  (2023-06-27T13:31:33Z)
• CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models [50.42886595228255]
  We propose to learn the desired text-audio correspondence by leveraging the visual modality as a bridge. We train a conditional diffusion model to generate the audio track of a video, given a video frame encoded by a pretrained contrastive language-image pretraining model.
  arXiv  Detail & Related papers  (2023-06-16T05:42:01Z)
• MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation [70.74377373885645]
  We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously. MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks.
  arXiv  Detail & Related papers  (2022-12-19T14:11:52Z)
• AudioGen: Textually Guided Audio Generation [116.57006301417306]
  We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive model that generates audio samples conditioned on text inputs.
  arXiv  Detail & Related papers  (2022-09-30T10:17:05Z)

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.