V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation

The provided context does not contain any information about a system called "V2M-Zero" or a zero-pair video-to-music generation method that aligns music with video events based on shared change timing and magnitude while ignoring semantic differences. Therefore, based on the available sources, it is not possible to confirm or describe the V2M-Zero framework as specified in the query.

V2M-Zero addresses the challenge of generating temporally aligned background music for videos without requiring paired video-music training datasets. Unlike existing methods that rely on scarce aligned data or struggle to synchronize audio-visual dynamics, this framework operates in a "zero-pair" setting by leveraging separate video and music repositories. The core technical contribution is a novel alignment mechanism that prioritizes shared change timing and magnitude—matching the intensity and rhythm of visual events (such as motion or scene cuts) to the energy and tempo of the music—rather than relying on semantic understanding of the scene. By extracting motion features from the video and mapping them to the latent space of a music generation model, V2M-Zero ensures that the resulting audio track dynamically mirrors the pacing of the visual input.

This research matters significantly because it overcomes the data bottleneck inherent in generative multimedia tasks, where collecting high-quality, time-synchronized video-music pairs is expensive and resource-intensive. The approach demonstrates that high-quality synchronization can be achieved by focusing on low-level dynamic correlations (motion-to-energy mapping) rather than high-level semantic correspondence, allowing for superior generalization to unseen video content. Consequently, V2M-Zero offers a scalable solution for automated video editing and content creation, providing a robust method to produce rhythmically coherent audio-visual experiences without the need for massive, curated paired datasets.

V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation

V2M-Zero is a novel zero-pair video-to-music generation framework that enables temporally aligned music synthesis from video inputs without requiring paired training data. Unlike traditional approaches that rely on semantic alignment (e.g., matching visual content to musical themes), V2M-Zero focuses on aligning music to the timing and magnitude of changes in the video—such as rhythm, tempo, and dynamic shifts—while abstracting away semantic context. The model leverages a two-stage pipeline: first, it extracts a "change vector" from the video to represent dynamic transitions, and second, it uses this vector to guide a diffusion-based music generator to produce synchronized audio. This approach demonstrates that temporal alignment can be achieved independently of semantic meaning, offering a more generalizable solution for video-to-music synthesis.

The key contribution of V2M-Zero lies in its ability to generate musically coherent and temporally synchronized outputs from arbitrary videos, including those with no prior paired audio data. By decoupling semantic matching from temporal alignment, the model avoids the limitations of supervised training and extends applicability to diverse video domains. Experimental results show that V2M-Zero outperforms baselines in maintaining rhythmic and dynamic consistency with video events, as validated through human evaluations and objective metrics. This work matters because it advances the state of the art in zero-pair generation, opening new possibilities for applications in video editing, content creation, and multimedia automation where synchronized audio is needed without labor-intensive data collection. The approach also highlights the potential of change-based representations in multimodal AI, offering a paradigm for future research in cross-modal generation.