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CLIP-RL: Closed-Loop Video Inpainting with Detection-Guided Reinforcement Learning.

Meng Wang1, Jing Ren1, Bing Wang1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 727 South Jingming Road, Kunming 650500, China.

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|January 28, 2026
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Summary
This summary is machine-generated.

This study introduces CLIP-RL, a novel reinforcement learning framework for video inpainting. CLIP-RL optimizes inpainting strategies adaptively, significantly improving temporal consistency and visual quality in video restoration tasks.

Keywords:
closed-loop frameworkinpainting detectionreinforcement learningtemporal consistencyvideo inpainting

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Existing video inpainting methods often fail to adapt to diverse scenarios, leading to temporal inconsistencies and quality degradation.
  • These methods struggle with capturing high-level temporal semantics crucial for realistic video restoration.

Purpose of the Study:

  • To introduce a novel reinforcement learning framework for adaptive video inpainting strategy optimization.
  • To address limitations in current methods regarding temporal consistency and semantic understanding.

Main Methods:

  • Reformulated video inpainting as an agent-environment interaction within a closed-loop framework (CLIP-RL).
  • Employed a policy network and a composite reward function with temporal alignment loss for adaptive strategy selection.
  • Utilized a pre-trained inpainting detection module for real-time quality feedback.

Main Results:

  • CLIP-RL improved Peak Signal-to-Noise Ratio (PSNR) from 34.43 to 34.67 and Structural Similarity Index Measure (SSIM) from 0.974 to 0.986 on the YouTube-VOS dataset compared to ProPainter.
  • Demonstrated superior performance in detail preservation and artifact suppression through qualitative analysis.

Conclusions:

  • CLIP-RL offers an effective adaptive strategy optimization for video inpainting.
  • The reinforcement learning approach enhances temporal consistency and overall visual quality in video restoration.