  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.
