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Y
Yan-Bo Lin
UNC Chapel Hill
J
Jonah Casebeer
Adobe Research
L
Long Mai
Adobe Research
A
Aniruddha Mahapatra
Adobe Research
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References (100)
[3]
Video Echoed in Music: Semantic, Temporal, and Rhythmic Alignment for Video-to-Music Generation
Founder's Pitch
"V2M-Zero enables zero-pair video-to-music generation for seamless time-aligned music synchronization in videos without paired data."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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Why It Matters
V2M-Zero addresses a significant gap in video content creation by enabling precise time-aligned music generation for video events without requiring paired datasets. This facilitates easier and more creative audiovisual production, catering to both personal and professional content creators.
Product Angle
Productize V2M-Zero as an API or plugin for video editing platforms, enabling users to easily generate and integrate time-synchronized background music into their videos.
Disruption
This technology could disrupt traditional music production and editing processes by eliminating the need for manual syncing and allowing creators to produce compelling multimedia content more efficiently.
Product Opportunity
The demand for seamless video and music integration is high in the digital content creation space, including social media influencers, marketing agencies, and film editors. These users can significantly benefit from an automated and efficient way to synchronize video events with music, leading to better engagement.
Use Case Idea
Create a plugin for video editing software that automatically generates and syncs music with uploaded video content, saving creators time and improving production quality.
Science
V2M-Zero uses event curves derived from intra-modal similarity to align music with video events. By measuring temporal changes within each modality independently, these curves provide comparable representations. This allows the system to fine-tune a text-to-music model on music-event curves and then swap in video-event curves at inference for synchronizing music with videos without requiring any cross-modal training.
Method & Eval
The study uses benchmarks such as OES-Pub, MovieGenBench-Music, and AIST++ to evaluate V2M-Zero against paired-data baselines. It shows substantial improvements in audio quality, semantic alignment, temporal synchronization, and beat alignment. The system also received positive assessments in a large crowd-source subjective listening test.
Caveats
The approach is highly dependent on the quality of the pretrained music and video encoders used to generate event curves. There could be challenges in handling very complex video scenes where event segmentation is not clear, which might affect synchronization accuracy.
Author Intelligence
Yan-Bo Lin
UNC Chapel Hill
Jonah Casebeer
Adobe Research
Long Mai
Adobe Research
Aniruddha Mahapatra
Adobe Research
Gedas Bertasius
UNC Chapel Hill
Nicholas J. Bryan
Adobe Research