Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion
  Latent Aligners
Video and audio content creation serves as the core technique for the movie
industry and professional users. Recently, existing diffusion-based methods
tackle video and audio generation separately, which hinders the technique
transfer from academia to industry. In this work, we aim at filling the gap,
with a carefully designed optimization-based framework for cross-visual-audio
and joint-visual-audio generation. We observe the powerful generation ability
of off-the-shelf video or audio generation models. Thus, instead of training
the giant models from scratch, we propose to bridge the existing strong models
with a shared latent representation space. Specifically, we propose a
multimodality latent aligner with the pre-trained ImageBind model. Our latent
aligner shares a similar core as the classifier guidance that guides the
diffusion denoising process during inference time. Through carefully designed
optimization strategy and loss functions, we show the superior performance of
our method on joint video-audio generation, visual-steered audio generation,
and audio-steered visual generation tasks. The project website can be found at
https://yzxing87.github.io/Seeing-and-Hearing/
65deb3413aec0530561b9bc5
Yazhou Xing
65deb3413aec0530561b9bc6
Yingqing He
admin_assigned
2024-02-28T12:33:53.827Z
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He
user
Yingqing
65deb3413aec0530561b9bc7
Zeyue Tian
claimed_verified
2025-03-16T21:25:05.894Z
6504409b52ca06fef957266d
/avatars/ac96eab7318cd8d472601cf7e7c50c97.svg
Zeyue
user
Zeyue7
65deb3413aec0530561b9bc8
Xintao Wang
admin_assigned
2024-02-28T12:34:07.511Z
60e272ca6c78a8c122b12127
https://cdn-avatars.huggingface.co/v1/production/uploads/60e272ca6c78a8c122b12127/xldEGBzGrU-bX6IwAw0Ie.jpeg
Xintao Wang
user
Xintao
65deb3413aec0530561b9bc9
Qifeng Chen
admin_assigned
2024-02-28T12:34:13.723Z
6467b121e7a6a374fd19b44b
/avatars/3f2874d58986d651aef55e3408b05700.svg
Qifeng Chen
user
cqf
Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion
  Latent Aligners
Video and audio content creation serves as the core technique for the movie
industry and professional users. Recently, existing diffusion-based methods
tackle video and audio generation separately, which hinders the technique
transfer from academia to industry. In this work, we aim at filling the gap,
with a carefully designed optimization-based framework for cross-visual-audio
and joint-visual-audio generation. We observe the powerful generation ability
of off-the-shelf video or audio generation models. Thus, instead of training
the giant models from scratch, we propose to bridge the existing strong models
with a shared latent representation space. Specifically, we propose a
multimodality latent aligner with the pre-trained ImageBind model. Our latent
aligner shares a similar core as the classifier guidance that guides the
diffusion denoising process during inference time. Through carefully designed
optimization strategy and loss functions, we show the superior performance of
our method on joint video-audio generation, visual-steered audio generation,
and audio-steered visual generation tasks. The project website can be found at
https://yzxing87.github.io/Seeing-and-Hearing/
A framework that integrates pre-trained video and audio generation models via a shared latent space to achieve joint and cross-modal content creation outperforms separate approaches.
diffusion-based methods cross-visual-audio joint-visual-audio generation off-the-shelf models multimodality latent aligner ImageBind model classifier guidance diffusion denoising visual-steered audio generation audio-steered visual generation