DTVNet: Dynamic Time-lapse Video Generation via Single Still Image

ECCV 2020 Jiangning ZhangChao XuLiang LiuMengmeng WangXia WuYong LiuYunliang Jiang

This paper presents a novel end-to-end dynamic time-lapse video generation framework, named DTVNet, to generate diversified time-lapse videos from a single landscape image, which are conditioned on normalized motion vectors. The proposed DTVNet consists of two submodules: \emph{Optical Flow Encoder} (OFE) and \emph{Dynamic Video Generator} (DVG)... (read more)

PDF Abstract ECCV 2020 PDF ECCV 2020 Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet