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Learning Fine-Grained Motion Embedding for Landscape Animation
[article]
2021
arXiv
pre-print
In this paper we focus on landscape animation, which aims to generate time-lapse videos from a single landscape image. Motion is crucial for landscape animation as it determines how objects move in videos. Existing methods are able to generate appealing videos by learning motion from real time-lapse videos. However, current methods suffer from inaccurate motion generation, which leads to unrealistic video results. To tackle this problem, we propose a model named FGLA to generate high-quality
arXiv:2109.02216v2
fatcat:idwxghazbzhcnfpoufdasw5phi