SOURCE-LINKED INTELLIGENCE
VIBE: Video Instruction-aligned Background music gEneration
Current video-to-music (V2M) models lack semantic control and fail to penalize instruction violations, largely due to their reliance on reconstruction objectives and the representational bottleneck of static cross-modal conditioning in Diffusion Autoregressive (DAR) architectures. To resolve this, we introduce VIBE, a novel text-and-video-to-music (T+V2M) generation model that leverages: (1) Conditioning Connection, a depth-wise cross-layer conditioning mechanism that dynamically bridges the planning and diffusion refinement heads and (2) a comprehensive reward modeling taxonomy, optimizing fo
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T01:19:55.000Z
First collected: 2026-09-21T07:22:03.933Z. This is not the publication date.