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

arXiv 1611.10314 ↗First posted 30 Nov 2016cs.CVcited by 44

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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)

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

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Citing papers per year: 2021: 3; 2022: 6; 2023: 18; 2024: 13; 2025: 3; 2026: 1.

44 papers.

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  1. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,742
  2. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,476
  3. 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 961
  4. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 658
  5. 2021GODIVA: Generating Open-DomaIn Videos from nAtural Descriptionscited by 256
  6. 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
  7. 2023Conditional Image-to-Video Generation with Latent Flow Diffusion Modelscited by 181
  8. 2023NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generationcited by 171
  9. 2023Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoisingcited by 139
  10. 2023Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generationcited by 137
  11. 2023Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfercited by 124
  12. 2024Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesiscited by 105
32 more
  1. 2023CelebV-Text: A Large-Scale Facial Text-Video Datasetcited by 104
  2. 2023SimDA: Simple Diffusion Adapter for Efficient Video Generationcited by 101
  3. 2021Make It Move: Controllable Image-to-Video Generation with Text Descriptionscited by 88
  4. 2023InstructVideo: Instructing Video Diffusion Models with Human Feedbackcited by 86
  5. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  6. 2023ARTboldsymbol·V: Auto-Regressive Text-to-Video Generation with Diffusion Modelscited by 76
  7. 2023VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generationcited by 74
  8. 2023GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generationcited by 36
  9. 20243D-SceneDreamer: Text-Driven 3D-Consistent Scene Generationcited by 29
  10. 2024From Sora What We Can See: A Survey of Text-to-Video Generationcited by 27
  11. 2022MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENerationcited by 20
  12. 2023VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning on Language-Video Foundation Modelscited by 16
  13. 2024I4VGen: Image as Free Stepping Stone for Text-to-Video Generationcited by 14
  14. 2024MotiF: Making Text Count in Image Animation with Motion Focal Losscited by 10
  15. 2024Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metriccited by 9
  16. 2023FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipelinecited by 8
  17. 2024Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unificationcited by 8
  18. 2025FreePCA: Integrating Consistency Information across Long-short Frames in Training-free Long Video Generation via Principal Component Analysiscited by 8
  19. 2023Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODEcited by 7
  20. 2023Story Visualization by Online Text Augmentation with Context Memorycited by 7
  21. 2023A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspectivecited by 6
  22. 2025ASTRA: Let Arbitrary Subjects Transform in Video Editingcited by 6
  23. 2024Text-to-Image Cross-Modal Generation: A Systematic Reviewcited by 5
  24. 2022Text-driven Video Predictioncited by 4
  25. 2024Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability,Reproducibility, and Practicalitycited by 4
  26. 2024InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructionscited by 3
  27. 2024ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancementcited by 3
  28. 2024UniVG: Towards UNIfied-modal Video Generationcited by 2
  29. 2026KVAE: Family of Tokenizers for Multimodal Generative Modelscited by 1
  30. 2022TPA-Net: Generate A Dataset for Text to Physics-based Animationcited by 0
  31. 2024ConCLVD: Controllable Chinese Landscape Video Generation via Diffusion Modelcited by 0
  32. 2025VidCLearn: A Continual Learning Approach for Text-to-Video Generationcited by 0