# Xp-GAN: Unsupervised Multi-object Controllable Video Generation

arXiv: https://arxiv.org/abs/2111.10233 · HTML: https://mlsanity.com/paper/2111.10233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [Temporal Generative Adversarial Nets with Singular Value Clipping](https://mlsanity.com/paper/1611.06624/) (2016)
- [Video Generation From Text](https://mlsanity.com/paper/1710.00421/) (2017)
- [Recycle-GAN: Unsupervised Video Retargeting](https://mlsanity.com/paper/1808.05174/) (2018)
- [Learning to Simulate Dynamic Environments with GameGAN](https://mlsanity.com/paper/2005.12126/) (2020)
- [Playable Video Generation](https://mlsanity.com/paper/2101.12195/) (2021)
- [Video Generation from Single Semantic Label Map](https://mlsanity.com/paper/1903.04480/) (2019)
- [Mocycle-GAN: Unpaired Video-to-Video Translation](https://mlsanity.com/paper/1908.09514/) (2019)
- [Lower Dimensional Kernels for Video Discriminators](https://mlsanity.com/paper/1912.08860/) (2019)
- [Attentive Semantic Video Generation using Captions](https://mlsanity.com/paper/1708.05980/) (2017)
- [DEVDAN: Deep Evolving Denoising Autoencoder](https://mlsanity.com/paper/1910.04062/) (2019)
- [Blind Denoising Autoencoder](https://mlsanity.com/paper/1912.07358/) (2019)
- [Point-to-Point Video Generation](https://mlsanity.com/paper/1904.02912/) (2019)
- [Unsupervised Bi-directional Flow-based Video Generation from one Snapshot](https://mlsanity.com/paper/1903.00913/) (2019)

## Abstract (arXiv)

Video Generation is a relatively new and yet popular subject in machine learning due to its vast variety of potential applications and its numerous challenges. Current methods in Video Generation provide the user with little or no control over the exact specification of how the objects in the generate video are to be moved and located at each frame, that is, the user can't explicitly control how each object in the video should move. In this paper we propose a novel method that allows the user to move any number of objects of a single initial frame just by drawing bounding boxes over those objects and then moving those boxes in the desired path. Our model utilizes two Autoencoders to fully decompose the motion and content information in a video and achieves results comparable to well-known baseline and state of the art methods.