# Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures

arXiv: https://arxiv.org/abs/1611.10314 · HTML: https://mlsanity.com/paper/1611.10314/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

## Built on by (most cited first, 30 of 44)

- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions](https://mlsanity.com/paper/2104.14806/) (2021)
- [Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer](https://mlsanity.com/paper/2204.03638/) (2022)
- [Conditional Image-to-Video Generation with Latent Flow Diffusion Models](https://mlsanity.com/paper/2303.13744/) (2023)
- [NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation](https://mlsanity.com/paper/2303.12346/) (2023)
- [Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising](https://mlsanity.com/paper/2305.18264/) (2023)
- [Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation](https://mlsanity.com/paper/2304.08477/) (2023)
- [Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer](https://mlsanity.com/paper/2311.17009/) (2023)
- [Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis](https://mlsanity.com/paper/2402.14797/) (2024)
- [CelebV-Text: A Large-Scale Facial Text-Video Dataset](https://mlsanity.com/paper/2303.14717/) (2023)
- [SimDA: Simple Diffusion Adapter for Efficient Video Generation](https://mlsanity.com/paper/2308.09710/) (2023)
- [Make It Move: Controllable Image-to-Video Generation with Text Descriptions](https://mlsanity.com/paper/2112.02815/) (2021)
- [InstructVideo: Instructing Video Diffusion Models with Human Feedback](https://mlsanity.com/paper/2312.12490/) (2023)
- [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)
- [ARTboldsymbol·V: Auto-Regressive Text-to-Video Generation with Diffusion Models](https://mlsanity.com/paper/2311.18834/) (2023)
- [VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation](https://mlsanity.com/paper/2309.00398/) (2023)
- [GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation](https://mlsanity.com/paper/2311.16511/) (2023)
- [3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation](https://mlsanity.com/paper/2403.09439/) (2024)
- [From Sora What We Can See: A Survey of Text-to-Video Generation](https://mlsanity.com/paper/2405.10674/) (2024)
- [MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration](https://mlsanity.com/paper/2204.08058/) (2022)
- [VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning on Language-Video Foundation Models](https://mlsanity.com/paper/2311.00990/) (2023)
- [I4VGen: Image as Free Stepping Stone for Text-to-Video Generation](https://mlsanity.com/paper/2406.02230/) (2024)
- [MotiF: Making Text Count in Image Animation with Motion Focal Loss](https://mlsanity.com/paper/2412.16153/) (2024)
- [Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric](https://mlsanity.com/paper/2411.16619/) (2024)
- [FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipeline](https://mlsanity.com/paper/2311.13073/) (2023)
- [Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unification](https://mlsanity.com/paper/2409.14993/) (2024)
- [FreePCA: Integrating Consistency Information across Long-short Frames in Training-free Long Video Generation via Principal Component Analysis](https://mlsanity.com/paper/2505.01172/) (2025)

## Abstract (arXiv)

This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the first approach of its kind. It combines a Variational Autoencoder~(VAE) with a Recurrent Attention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. The recurrent attention mechanism in Sync-DRAW attends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is efficient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets. (Accepted as oral paper in ACM-Multimedia 2017)