arXiv (All)
open access
AV-FlowEdit: Training-Free Prompt-Driven Audio-Visual Editing with Event-Aware Spectral Trajectory Preservation
Joint audio-video diffusion models generate synchronized content, but editing existing clips requires target changes without disrupting non-target structure or cross-modal correspondence. We present AV-FlowEdit, a training-free and inversion-free framework for prompt-driven audio-video editing. Our
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Salt++: Context-Aligned Post-Training for Few-Step Streaming Multimodal Generation#1132196
- OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation#178906
- One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing#639700
- Intervention, Not Shared Latents: Blocking Visual Shortcuts in Audio-Video Generation#1210100
- Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation#1084393
Record · ID 1297424
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive