Semantic-Critical Regions for Video-to-Audio Generation

Key points

• In real-world videos, sound events are often associated with localized and fine-grained visual cues, such as object motion, object-part dynamics, contact events, and interactions between objects.1
• Existing video-to-audio (V2A) methods employ implicit conditioning based on global visual representations or explicit control derived from temporal and spatial cues.1
• We then transform the localized regions into a control representation through adapter-based dual visual encoding, which jointly captures global visual context and fine-grained sound-relevant evidence.1
• These results highlight the value of prioritizing fine-grained, sound-relevant visual regions for V2A generation.1

Sources (1)

• [1]Semantic-Critical Regions for Video-to-Audio Generation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 06:56 AM

  Motivated by this observation, we refer to regions containing such sound-relevant visual evidence as Semantic-Critical Regions (SCRs) and introduce SCR-V2A, a V2A generation framework that explicitly localizes and encodes SCRs as fine-grained visual guidance for audio generation.

  In real-world videos, sound events are often associated with localized and fine-grained visual cues, such as object motion, object-part dynamics, contact events, and interactions between objects.

Extractive summary: sentences quoted from the sources.