# Improving Video Generation for Multi-functional Applications

arXiv: https://arxiv.org/abs/1711.11453 · HTML: https://mlsanity.com/paper/1711.11453/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 9 of 9)

- [Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control](https://mlsanity.com/paper/2106.02019/) (2021)
- [Style and Pose Control for Image Synthesis of Humans from a Single Monocular View](https://mlsanity.com/paper/2102.11263/) (2021)
- [HumanGAN: A Generative Model of Humans Images](https://mlsanity.com/paper/2103.06902/) (2021)
- [HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars](https://mlsanity.com/paper/2112.10203/) (2021)
- [HumanLiff: Layer-wise 3D Human Generation with Diffusion Model](https://mlsanity.com/paper/2308.09712/) (2023)
- [EgoRenderer: Rendering Human Avatars from Egocentric Camera Images](https://mlsanity.com/paper/2111.12685/) (2021)
- [SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering](https://mlsanity.com/paper/2404.01225/) (2024)
- [LARNet: Latent Action Representation for Human Action Synthesis](https://mlsanity.com/paper/2110.10899/) (2021)
- [HumANDiff: Articulated Noise Diffusion for Motion-Consistent Human Video Generation](https://mlsanity.com/paper/2604.05961/) (2026)

## Abstract (arXiv)

In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications. Our improved video GAN model does not separate foreground from background nor dynamic from static patterns, but learns to generate the entire video clip conjointly. Our model can thus be trained to generate - and learn from - a broad set of videos with no restriction. This is achieved by designing a robust one-stream video generation architecture with an extension of the state-of-the-art Wasserstein GAN framework that allows for better convergence. The experimental results show that our improved video GAN model outperforms state-of-theart video generative models on multiple challenging datasets. Furthermore, we demonstrate the superiority of our model by successfully extending it to three challenging problems: video colorization, video inpainting, and future prediction. To the best of our knowledge, this is the first work using GANs to colorize and inpaint video clips.