mlsanityData to 7 Oct 2026

FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery

arXiv 2310.00106 ↗First posted 29 Sep 2023cs.CV cs.AIcited by 1

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

Our study introduces a new image-to-video generator called FashionFlow to generate fashion videos. By utilising a diffusion model, we are able to create short videos from still fashion images. Our approach involves developing and connecting relevant components with the diffusion model, which results in the creation of high-fidelity videos that are aligned with the conditional image. The components include the use of pseudo-3D convolutional layers to generate videos efficiently. VAE and CLIP encoders capture vital characteristics from still images to condition the diffusion model at a global level. Our research demonstrates a successful synthesis of fashion videos featuring models posing from various angles, showcasing the fit and appearance of the garment. Our findings hold great promise for improving and enhancing the shopping experience for the online fashion industry.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 58 of its 77 references that are in the map. First posted on arXiv 29 Sep 2023.

Builds on

58 of 77 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion12 Apr
  2. 2023DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models14 Feb
  3. 2022Person Image Synthesis via Denoising Diffusion Model22 Nov

Other references · 55

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  6. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  9. 2014Going Deeper with Convolutionscited by 4,231
  10. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  11. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  12. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
38 more
  1. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  2. 2016Improved Techniques for Training GANscited by 2,938
  3. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  4. 2022Video Diffusion Modelscited by 2,280
  5. 2014Conditional Generative Adversarial Netscited by 2,152
  6. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  7. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
  8. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
  9. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
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  12. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,139
  13. 2022Diffusion Models in Vision: A Surveycited by 1,008
  14. 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 958
  15. 2015Image-based Recommendations on Styles and Substitutescited by 702
  16. 2023Structure and Content-Guided Video Synthesis with Diffusion Modelscited by 589
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  20. 2016Generating Videos with Scene Dynamicscited by 376
  21. 2023VideoFusion: Decomposed Diffusion Models for High-Quality Video Generationcited by 330
  22. 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 300
  23. 2021VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalizationcited by 287
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  25. 2023Preserve Your Own Correlation: A Noise Prior for Video Diffusion Modelscited by 284
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  27. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
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  29. 2019Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesiscited by 161
  30. 2020Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Contentcited by 148
  31. 2018StoryGAN: A Sequential Conditional GAN for Story Visualizationcited by 137
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  33. 2019Towards Multi-pose Guided Virtual Try-on Networkcited by 95
  34. 2018Recycle-GAN: Unsupervised Video Retargetingcited by 89
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  38. 2019Mocycle-GAN: Unpaired Video-to-Video Translationcited by 37

50 most cited of 55 All 55 (JSON)

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositionscited by 0

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