Video-to-Audio Generation with Hidden Alignment
Generating semantically and temporally aligned audio content in accordance
with video input has become a focal point for researchers, particularly
following the remarkable breakthrough in text-to-video generation. In this
work, we aim to offer insights into the video-to-audio generation paradigm,
focusing on three crucial aspects: vision encoders, auxiliary embeddings, and
data augmentation techniques. Beginning with a foundational model VTA-LDM built
on a simple yet surprisingly effective intuition, we explore various vision
encoders and auxiliary embeddings through ablation studies. Employing a
comprehensive evaluation pipeline that emphasizes generation quality and
video-audio synchronization alignment, we demonstrate that our model exhibits
state-of-the-art video-to-audio generation capabilities. Furthermore, we
provide critical insights into the impact of different data augmentation
methods on enhancing the generation framework's overall capacity. We showcase
possibilities to advance the challenge of generating synchronized audio from
semantic and temporal perspectives. We hope these insights will serve as a
stepping stone toward developing more realistic and accurate audio-visual
generation models.
668f41aa52a697a87e3c31e3
Manjie Xu
claimed_verified
2024-07-11T13:45:44.890Z
627df0bd72aed9ffb0a96a8a
/avatars/a863f3cf791f3f525df6863f8d4c476b.svg
ariesssxu
user
ariesssxu
668f41aa52a697a87e3c31e4
Chenxing Li
admin_assigned
2024-07-11T10:23:59.730Z
64cc9972e60d2cddfadc5c35
/avatars/5dac96288fa93fa965f1b57ad8d0a9b7.svg
Chenxing
user
Chenxinglili
668f41aa52a697a87e3c31e5
Yong Ren
668f41aa52a697a87e3c31e6
Rilin Chen
668f41aa52a697a87e3c31e7
Yu Gu
668f41aa52a697a87e3c31e8
Wei Liang
668f41aa52a697a87e3c31e9
Dong Yu
Video-to-Audio Generation with Hidden Alignment
Generating semantically and temporally aligned audio content in accordance
with video input has become a focal point for researchers, particularly
following the remarkable breakthrough in text-to-video generation. In this
work, we aim to offer insights into the video-to-audio generation paradigm,
focusing on three crucial aspects: vision encoders, auxiliary embeddings, and
data augmentation techniques. Beginning with a foundational model VTA-LDM built
on a simple yet surprisingly effective intuition, we explore various vision
encoders and auxiliary embeddings through ablation studies. Employing a
comprehensive evaluation pipeline that emphasizes generation quality and
video-audio synchronization alignment, we demonstrate that our model exhibits
state-of-the-art video-to-audio generation capabilities. Furthermore, we
provide critical insights into the impact of different data augmentation
methods on enhancing the generation framework's overall capacity. We showcase
possibilities to advance the challenge of generating synchronized audio from
semantic and temporal perspectives. We hope these insights will serve as a
stepping stone toward developing more realistic and accurate audio-visual
generation models.
The study explores vision encoders, auxiliary embeddings, and data augmentation to enhance video-to-audio generation quality and synchronization, demonstrating state-of-the-art capabilities with the VTA-LDM model.
vision encoders auxiliary embeddings data augmentation video-to-audio generation ablation studies evaluation pipeline video-audio synchronization semantic generation temporal generation