# Unsupervised Bi-directional Flow-based Video Generation from one Snapshot

arXiv: https://arxiv.org/abs/1903.00913 · HTML: https://mlsanity.com/paper/1903.00913/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance](https://mlsanity.com/paper/2110.07460/) (2021)
- [Xp-GAN: Unsupervised Multi-object Controllable Video Generation](https://mlsanity.com/paper/2111.10233/) (2021)

## Abstract (arXiv)

Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully generate novel frames without visual distortions. In this work, we leverage an unsupervised variational model to learn rich motion patterns in the form of long-term bi-directional flow fields, and apply the predicted flows to generate high-quality video sequences. In contrast to the state-of-the-art approach, our method does not require external flow supervisions for learning. This is achieved through a novel module that performs bi-directional flows prediction from a single image. In addition, with the bi-directional flow consistency check, our method can handle occlusion and warping artifacts in a principled manner. Our method can be trained end-to-end based on arbitrarily sampled natural video clips, and it is able to capture multi-modal motion uncertainty and synthesizes photo-realistic novel sequences. Quantitative and qualitative evaluations over synthetic and real-world datasets demonstrate the effectiveness of the proposed approach over the state-of-the-art methods.