mlsanityData to 7 Oct 2026

Attentive Semantic Video Generation using Captions

arXiv 1708.05980 ↗First posted 20 Aug 2017cs.CVcited by 28

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Abstract (arXiv)

This paper proposes a network architecture to perform variable length semantic video generation using captions. We adopt a new perspective towards video generation where we allow the captions to be combined with the long-term and short-term dependencies between video frames and thus generate a video in an incremental manner. Our experiments demonstrate our network architecture's ability to distinguish between objects, actions and interactions in a video and combine them to generate videos for unseen captions. The network also exhibits the capability to perform spatio-temporal style transfer when asked to generate videos for a sequence of captions. We also show that the network's ability to learn a latent representation allows it generate videos in an unsupervised manner and perform other tasks such as action recognition. (Accepted in International Conference in Computer Vision (ICCV) 2017)

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2017.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 9; 2024: 4; 2025: 1.

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  1. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
  2. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
  3. 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 958
  4. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
  5. 2023Conditional Image-to-Video Generation with Latent Flow Diffusion Modelscited by 180
  6. 2023Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoisingcited by 135
  7. 2023SimDA: Simple Diffusion Adapter for Efficient Video Generationcited by 101
  8. 2021Make It Move: Controllable Image-to-Video Generation with Text Descriptionscited by 88
  9. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  10. 2023ARTboldsymbol·V: Auto-Regressive Text-to-Video Generation with Diffusion Modelscited by 74
  11. 2022Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioningcited by 37
  12. 2022Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generationcited by 32
16 more
  1. 20243D-SceneDreamer: Text-Driven 3D-Consistent Scene Generationcited by 29
  2. 2023AI-Generated Content (AIGC) for Various Data Modalities: A Surveycited by 25
  3. 2021Learning Semantic-Aware Dynamics for Video Predictioncited by 21
  4. 2023VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning on Language-Video Foundation Modelscited by 16
  5. 2021Action2video: Generating Videos of Human 3D Actionscited by 15
  6. 2025ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidancecited by 11
  7. 2021M3L: Language-based Video Editing via Multi-Modal Multi-Level Transformerscited by 8
  8. 2024Text-to-Image Cross-Modal Generation: A Systematic Reviewcited by 5
  9. 2022Text-driven Video Predictioncited by 4
  10. 2023Text-guided 3D Human Generation from 2D Collectionscited by 4
  11. 2024Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability,Reproducibility, and Practicalitycited by 4
  12. 2021Video Generation from Text Employing Latent Path Construction for Temporal Modelingcited by 1
  13. 2022Disentangling Content and Motion for Text-Based Neural Video Manipulationcited by 1
  14. 2021Xp-GAN: Unsupervised Multi-object Controllable Video Generationcited by 0
  15. 2022Autoencoding Video Latents for Adversarial Video Generationcited by 0
  16. 2024ConCLVD: Controllable Chinese Landscape Video Generation via Diffusion Modelcited by 0