A Survey on Video Prediction: From Deterministic to Generative Approaches
CoRR(2024)
摘要
Video prediction, a fundamental task in computer vision, aims to enable
models to generate sequences of future frames based on existing video content.
This task has garnered widespread application across various domains. In this
paper, we comprehensively survey both historical and contemporary works in this
field, encompassing the most widely used datasets and algorithms. Our survey
scrutinizes the challenges and evolving landscape of video prediction within
the realm of computer vision. We propose a novel taxonomy centered on the
stochastic nature of video prediction algorithms. This taxonomy accentuates the
gradual transition from deterministic to generative prediction methodologies,
underlining significant advancements and shifts in approach.
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