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Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques

Shreyank N Gowda, Yash Thakre, Shashank Narayana Gowda, Xiaobo Jin

TL;DR

The paper addresses the problem of video inpainting, detailing missing or corrupted regions in video sequences. It surveys state-of-the-art approaches by organizing them into patch-based, motion-based, and diffusion-based methods, while discussing applications and underlying principles. A practical evaluation reimplements five representative methods and benchmarks both perceptual quality via human annotators and computational efficiency on standardized hardware, illustrating the quality-efficiency trade-off. The study highlights diffusion-guided and flow-aware techniques as promising directions and provides guidelines to advance research and deployment in real-world scenarios.

Abstract

This paper offers a comprehensive analysis of recent advancements in video inpainting techniques, a critical subset of computer vision and artificial intelligence. As a process that restores or fills in missing or corrupted portions of video sequences with plausible content, video inpainting has evolved significantly with the advent of deep learning methodologies. Despite the plethora of existing methods and their swift development, the landscape remains complex, posing challenges to both novices and established researchers. Our study deconstructs major techniques, their underpinning theories, and their effective applications. Moreover, we conduct an exhaustive comparative study, centering on two often-overlooked dimensions: visual quality and computational efficiency. We adopt a human-centric approach to assess visual quality, enlisting a panel of annotators to evaluate the output of different video inpainting techniques. This provides a nuanced qualitative understanding that complements traditional quantitative metrics. Concurrently, we delve into the computational aspects, comparing inference times and memory demands across a standardized hardware setup. This analysis underscores the balance between quality and efficiency: a critical consideration for practical applications where resources may be constrained. By integrating human validation and computational resource comparison, this survey not only clarifies the present landscape of video inpainting techniques but also charts a course for future explorations in this vibrant and evolving field.

Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques

TL;DR

The paper addresses the problem of video inpainting, detailing missing or corrupted regions in video sequences. It surveys state-of-the-art approaches by organizing them into patch-based, motion-based, and diffusion-based methods, while discussing applications and underlying principles. A practical evaluation reimplements five representative methods and benchmarks both perceptual quality via human annotators and computational efficiency on standardized hardware, illustrating the quality-efficiency trade-off. The study highlights diffusion-guided and flow-aware techniques as promising directions and provides guidelines to advance research and deployment in real-world scenarios.

Abstract

This paper offers a comprehensive analysis of recent advancements in video inpainting techniques, a critical subset of computer vision and artificial intelligence. As a process that restores or fills in missing or corrupted portions of video sequences with plausible content, video inpainting has evolved significantly with the advent of deep learning methodologies. Despite the plethora of existing methods and their swift development, the landscape remains complex, posing challenges to both novices and established researchers. Our study deconstructs major techniques, their underpinning theories, and their effective applications. Moreover, we conduct an exhaustive comparative study, centering on two often-overlooked dimensions: visual quality and computational efficiency. We adopt a human-centric approach to assess visual quality, enlisting a panel of annotators to evaluate the output of different video inpainting techniques. This provides a nuanced qualitative understanding that complements traditional quantitative metrics. Concurrently, we delve into the computational aspects, comparing inference times and memory demands across a standardized hardware setup. This analysis underscores the balance between quality and efficiency: a critical consideration for practical applications where resources may be constrained. By integrating human validation and computational resource comparison, this survey not only clarifies the present landscape of video inpainting techniques but also charts a course for future explorations in this vibrant and evolving field.
Paper Structure (26 sections, 4 equations, 3 figures, 2 tables)

This paper contains 26 sections, 4 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Fundamentals of Video Inpainting
  3. Taxonomy of Video Inpainting Techniques
  4. Patch-Based Methods
  5. Key Ideas
  6. Algorithms and Approaches
  7. Motion-Based Methods
  8. Key Ideas
  9. Algorithms and Approaches
  10. Diffusion-Based Methods for Video Inpainting
  11. Key Ideas
  12. Algorithms and Approaches
  13. Challenges in Video Inpainting
  14. Scene Complexity
  15. Motion Dynamics and Temporal Coherence
  16. ...and 11 more sections

Figures (3)

  • Figure 1: Comparison of different video inpainting methods on a flamingo video. Different frames are taken from the output and shown along with the original frame (ground truth).
  • Figure 2: Comparison of different video inpainting methods on a hiking video. Different frames are taken from the output and shown along with the original frame (ground truth).
  • Figure 3: Comparison of different video inpainting methods on a tennis video. Different frames are taken from the output and shown along with the original frame (ground truth).