# Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs

arXiv: https://arxiv.org/abs/1810.02419 · HTML: https://mlsanity.com/paper/1810.02419/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 30 of 36)

- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [MAGVIT: Masked Generative Video Transformer](https://mlsanity.com/paper/2212.05199/) (2022)
- [StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2](https://mlsanity.com/paper/2112.14683/) (2021)
- [Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer](https://mlsanity.com/paper/2204.03638/) (2022)
- [Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks](https://mlsanity.com/paper/2202.10571/) (2022)
- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [Video Probabilistic Diffusion Models in Projected Latent Space](https://mlsanity.com/paper/2302.07685/) (2023)
- [A Good Image Generator Is What You Need for High-Resolution Video Synthesis](https://mlsanity.com/paper/2104.15069/) (2021)
- [Diffusion Models for Video Prediction and Infilling](https://mlsanity.com/paper/2206.07696/) (2022)
- [Generating Long Videos of Dynamic Scenes](https://mlsanity.com/paper/2206.03429/) (2022)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [VIDM: Video Implicit Diffusion Models](https://mlsanity.com/paper/2212.00235/) (2022)
- [CCVS: Context-aware Controllable Video Synthesis](https://mlsanity.com/paper/2107.08037/) (2021)
- [Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation](https://mlsanity.com/paper/2309.03549/) (2023)
- [StyleVideoGAN: A Temporal Generative Model using a Pretrained StyleGAN](https://mlsanity.com/paper/2107.07224/) (2021)
- [Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning](https://mlsanity.com/paper/2203.02573/) (2022)
- [Dysen-VDM: Empowering Dynamics-aware Text-to-Video Diffusion with LLMs](https://mlsanity.com/paper/2308.13812/) (2023)
- [Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation](https://mlsanity.com/paper/2211.12824/) (2022)
- [TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation](https://mlsanity.com/paper/2405.04682/) (2024)
- [InMoDeGAN: Interpretable Motion Decomposition Generative Adversarial Network for Video Generation](https://mlsanity.com/paper/2101.03049/) (2021)
- [3D-Aware Video Generation](https://mlsanity.com/paper/2206.14797/) (2022)
- [MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation](https://mlsanity.com/paper/2502.12632/) (2025)
- [PAC-Bayesian Generalization Bounds for Adversarial Generative Models](https://mlsanity.com/paper/2302.08942/) (2023)
- [SSGVS: Semantic Scene Graph-to-Video Synthesis](https://mlsanity.com/paper/2211.06119/) (2022)
- [Layered Controllable Video Generation](https://mlsanity.com/paper/2111.12747/) (2021)
- [Evolution of Video Generative Foundations](https://mlsanity.com/paper/2604.06339/) (2026)
- [VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts](https://mlsanity.com/paper/2103.07292/) (2021)
- [Controllable Video Generation through Global and Local Motion Dynamics](https://mlsanity.com/paper/2204.06558/) (2022)
- [MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs](https://mlsanity.com/paper/2303.02906/) (2023)

## Abstract (arXiv)

The extension of image generation to video generation turns out to be a very difficult task, since the temporal dimension of videos introduces an extra challenge during the generation process. Besides, due to the limitation of memory and training stability, the generation becomes increasingly challenging with the increase of the resolution/duration of videos. In this work, we exploit the idea of progressive growing of Generative Adversarial Networks (GANs) for higher resolution video generation. In particular, we begin to produce video samples of low-resolution and short-duration, and then progressively increase both resolution and duration alone (or jointly) by adding new spatiotemporal convolutional layers to the current networks. Starting from the learning on a very raw-level spatial appearance and temporal movement of the video distribution, the proposed progressive method learns spatiotemporal information incrementally to generate higher resolution videos. Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution. SWGAN loss replaces the distance between joint distributions by that of one-dimensional marginal distributions, making the loss easier to compute. We evaluate the proposed model on our collected face video dataset of 10,900 videos to generate photorealistic face videos of 256x256x32 resolution. In addition, our model also reaches a record inception score of 14.57 in unsupervised action recognition dataset UCF-101.