FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry
In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and image modalities within a single model. Current approaches to collecting video editing data typically depend on labour-intensive, time-consuming curated procedures--involving object mask annotation, the use of error-introducing pair synthesis via I2V model and ControlNet-like guidance, and VLM-based quality filtering or refinement--and demonstrate limited task scalability. As a result, the diversity of editing tasks remains substantially narrower than t
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- PossiblePossibly related (embedding) · 53%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzySimilar title/name (fuzzy) · 59%halfrost/Halfrost-Field →
“Fuzzy title match (0.73): “FlowMimic: Mask-free Visual Editing and Generation with Pixe” ≈ “halfrost/Halfrost-Field””
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “FlowMimic: Mask-free Visual Editing and Generation with Pixe” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Dingyun Zhang →
“FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Gener”
- LinkedLinked via arxiv author · 85%Lixue Gong →
“FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Gener”
- LinkedLinked via arxiv author · 85%Shiwei Liu →
“FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Gener”