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Deep Video Harmonization with Color Mapping Consistency

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arxiv 2205.00687 v1 pith:GCFS6JWP submitted 2022-05-02 cs.CV cs.AI

Xinyuan Lu , Shengyuan Huang , Li Niu , Wenyan Cong , Liqing Zhang This is my paper
classification cs.CVcs.AI
keywords videoharmonizationdatasetcolorconsistencymappingcompositeforeground
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Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background. So far, video harmonization has only received limited attention and there is no public dataset for video harmonization. In this work, we construct a new video harmonization dataset HYouTube by adjusting the foreground of real videos to create synthetic composite videos. Moreover, we consider the temporal consistency in video harmonization task. Unlike previous works which establish the spatial correspondence, we design a novel framework based on the assumption of color mapping consistency, which leverages the color mapping of neighboring frames to refine the current frame. Extensive experiments on our HYouTube dataset prove the effectiveness of our proposed framework. Our dataset and code are available at https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FlexComposer: Unified Video Compositing from Images to Dynamic Footage with Flexible Trajectory Control

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A single video-diffusion framework composites both static images and dynamic footage along user-defined trajectories by transporting canonical foreground latents directly into the background latent sequence.

  2. Video Color Grading via Look-Up Table Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A diffusion model generates a 3D look-up table from the feature difference between a source frame and a reference frame to grade video colors while preserving structure and enabling text-based retouching.

  3. EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control

    eess.IV 2025-07 conditional novelty 4.0 of 10

    A training-free Lagrangian motion magnification framework with periodic reference resetting and tissue-aware dual-mask control improves vascular pulsation visibility in endoscopic surgery videos.

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