Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

PaintNet: A shape-constrained generative framework for generating clothing from fashion model

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recent years have witnessed the proliferation of online fashion blogs and communities, where a large amount of fashion model images with chic clothes in various scenarios are publicly available. To facilitate users to find the corresponding clothes, we focus on studying how to generate pure wellshaped clothing items with the best view from the complex model images. Towards this end, inspired by painting, where the initial sketches and following coloring are both essential, we propose a two-stage shape-constrained clothing generative framework, dubbed as PaintNet. PaintNet comprises two coherent components: shape predictor and texture renderer. The shape predictor is devised to predict the intermediate shape map for the to-be-generated clothing item based on the latent representation learning, while the texture renderer is introduced to generate the final clothing image with the guidance of the predicted shape map. Extensive qualitative and quantitative experiments conducted on the public Lookbook dataset verify the effectiveness of PaintNet in clothing generation from fashion model images. Moreover, we also explore the potential of PaintNet in the task of cross-domain clothing retrieval, and the experiment results show that PaintNet can achieve, on average, 5.34% performance improvement over the traditional non-generative retrieval methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://www.chinainternetwatch.com/28092/retail-2018/.

  2. https://www.chictopia.com/.

  3. https://github.com/linjunyu/PaintNet.

References

  1. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. In: OSDI, pp 265–283

  2. Barnes C, Shechtman E, Finkelstein A, Goldman DB (2009) Patchmatch: A randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics, vol 28. ACM, pp 24

  3. Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: European conference on computer vision (ECCV), pp 404–417

  4. Chen H, Hui K, Wang S, Tsao L, Shuai H, Cheng W (2019) Beautyglow: On-Demand Makeup Transfer Framework With Reversible Generative Network. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 10042–10050

  5. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 886–893

  6. Emami H, Aliabadi MM, Dong M, Chinnam RB (2019) Spa-gan: Spatial attention gan for image-to-image translation. arXiv:1908.06616

  7. Gatys L, Ecker AS, Bethge M (2015) Texture synthesis using convolutional neural networks. In: Advances in neural information processing systems (neurIPS), pp 262–270

  8. Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 2414-2423

  9. Ge W (2018) Deep metric learning with hierarchical triplet loss. In: European conference on computer vision (ECCV), pp 269–285

  10. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems (neurIPS), pp 2672–2680

  11. Gordo A, Almazan J, Revaud J, Larlus D (2016) Deep image retrieval: Learning global representations for image search. In: European conference on computer vision (ECCV), pp 241–257

  12. Hadi Kiapour M, Han X, Lazebnik S, Berg AC, Berg TL (2015) Where to buy it: Matching street clothing photos in online shops. In: International conference on computer vision (ICCV), pp 3343–3351

  13. He K, Gkioxari G, Dollar P, Girshick R (2017) Mask r-cnn. In: International conference on computer vision (ICCV), pp 2961–2969

  14. Hidayati SC, You C, Cheng W, Hua K (2018) Learning and recognition of clothing genres from Full-Body images. IEEE transactions on Systems, Man, and Cybernetics 48:1647–1659

    Google Scholar 

  15. Hidayati SC, Hua K, Cheng W, Sun S (2014) What are the fashion trends in new york. In: ACM International conference on multimedia, pp 197–200

  16. Hoffer E, Ailon N (2015) Deep metric learning using triplet network. In: International conference on learning representations (ICLR)

  17. Hoffman J, Tzeng E, Park T, Zhu J, Isola P, Saenko K, Darrell T (2018) CyCADA: Cycle-consistent Adversarial Domain Adaptation. In: International conference on machine learning, pp 1989–1998

  18. Huang J, Feris RS, Chen Q, Yan S (2015) Cross-domain image retrieval with a dual attribute-aware ranking network. In: International conference on computer vision (ICCV), pp 1062–1070

  19. Hsieh C, Chen C, Chou C, Shuai H, Liu J, Cheng W (2019) Fashionon: Semantic-guided Image-based Virtual Try-on with Detailed Human and Clothing Information. In: ACM International conference on multimedia, pp 275–283

  20. Isola P, Zhu J, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1125–1134

  21. Jaderberg M, Simonyan K, Zisserman A, Kavukcuoglu K (2015) Spatial transformer networks. In: Advances in neural information processing systems (neurIPS), pp 2017–2025

  22. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision (ECCV), pp 694–711

  23. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980

  24. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (neurIPS), pp 1097–1105

  25. Li C, Wand M (2016) Precomputed real-time texture synthesis with markovian generative adversarial networks. In: European conference on computer vision (ECCV), pp 702–716

  26. Liu Z, Luo P, Qiu S, Wang X, Tang X (2016) Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1096–1104

  27. Lo L, Liu C, Lin R, Wu B, Shuai H, Cheng W (2019) Dressing for attention: Outfit based fashion popularity prediction. In: International conference on image processing (ICIP), pp 3222–3226

  28. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV) 60:91–110

    Article  Google Scholar 

  29. Marques O, Mayron LM, Borba GB, Gamba HR (2006) Using visual attention to extract regions of interest in the context of image retrieval. In: Annual southeast regional conference, pp 638–643

  30. Noh H, De Araujo AF, Sim J, Weyand T, Han B (2017) Large-scale image retrieval with attentive deep local features. In: IEEE International conference on computer vision (ICCV), pp 3456–3465

  31. Ono Y, Trulls E, Fua P, Yi KM (2018) Lf-net: Learning local features from images. In: Advances in neural information processing systems (neurIPS), pp 6234–6244

  32. Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. International Conference on Learning Representations (ICLR)

  33. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer assisted intervention (MICCAI), pp 234–241

  34. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Advances in neural information processing systems (neurIPS), pp 2234–2242

  35. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 815–823

  36. Simonyan K, Zisserman A (2015) Very deep convolutional networks for Large-Scale image recognition. In: International conference on learning representations

  37. Sonderby CK, Caballero J, Theis L, Shi W, Huszar F (2017) Amortised map inference for image super-resolution. In: International conference on learning representations (ICLR)

  38. Song Y, Li Y, Wu B, Chen C, Zhang X, Adam H (2017) Learning unified embedding for apparel recognition. In: IEEE International conference on computer vision (ICCV), pp 2243–2246

  39. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 2818–2826

  40. Tian Y, Fan B, Wu F (2017) L2-net: Deep learning of discriminative patch descriptor in euclidean space. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 6128–6136

  41. Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 2962–2971

  42. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP, et al. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing (TIP) 13:600–612

    Article  Google Scholar 

  43. Wang X, Sun Z, Zhang W, Zhou Y, Jiang YG (2016) Matching user photos to online products with robust deep features. In: International conference on multimedia retrieval (ICMR), pp 7–14

  44. Wu B, Zhou X, Zhao S, Yue X, Keutzer K (2019) Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In: International conference on robotics and automation (ICRA), pp 4376–4382

  45. Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning (ICML), pp 2048-2057

  46. Xu Y, Han Y, Hong R, Tian Q (2018) Sequential video VLAD: Training the aggregation locally and temporally. IEEE Transactions on Image Processing (TIP) 27:4933–4944

    Article  MathSciNet  Google Scholar 

  47. Yoo D, Kim N, Park S, Paek AS, Kweon IS (2016) Pixel-level domain transfer. In: European conference on computer vision (ECCV), pp 517–532

  48. Yuan Y, Yang K, Zhang C (2017) Hard-aware deeply cascaded embedding. In: IEEE International conference on computer vision (ICCV), pp 814–823

  49. Zhang H, Goodfellow IJ, Metaxas DN, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning (ICML), pp 7354–7363

  50. Zhu J, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: International conference on computer vision (ICCV), pp 2223–2232

  51. Zhao J, Mathieu M, LeCun Y (2016) Energy-based generative adversarial network. arXiv:1609.03126

  52. Zhao S, Liu Y, Han Y, Hong R, Hu Q, Tian Q (2018) Pooling the Convolutional Layers in Deep ConvNets for Video Action Recognition. IEEE Transactions on Circuits and Systems for Video Technology 28:1839–1849

    Article  Google Scholar 

  53. Zhao S, Zhao X, Ding G, Keutzer K (2018) EmotionGAN: Unsupervised Domain Adaptation for Learning Discrete Probability Distributions of Image Emotions. In: ACM International conference on multimedia, pp 1319–1327

  54. Zhao S, Lin C, Xu P, Zhao S, Guo Y, Krishna R, Keutzer K (2019) CycleemotionGAN: Emotional Semantic Consistency Preserved cycleGAN for Adapting Image Emotions. In: National conference on artificial intelligence, pp 2620–2627

  55. Zhao S, Li B, Yue X, Gu Y, Xu P, Hu R, Keutzer K (2019) Multi-source domain adaptation for semantic segmentation. In: Advances in neural information processing systems (neurIPS), pp 7285–7298

  56. Zhao S, Wang G, Zhang S, Gu Y, Li Y, Song Z, Keutzer K (2020) Multi-source Distilling Domain Adaptation. In: National conference on artificial intelligence

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China, No.: 61772310, No.:61702300, No.:61702302, No.: 61802231, and No. U1836216; the Project of Thousand Youth Talents 2016; the Shandong Provincial Natural Science and Foundation, No.: ZR2019JQ23, No.:ZR2019QF001; the Young Scholars Program of Shandong University.

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Shandong University, Qingdao, China

    Junyu Lin, Xuemeng Song, Tian Gan & Liqiang Nie

  2. State Grid Zhejiang Electric Power Company, Hangzhou, China

    Yiyang Yao

  3. Department of Computer Science and Technology, China University of Petroleum, Beijing, China

    Weifeng Liu

Authors
  1. Junyu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuemeng Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tian Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiyang Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weifeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liqiang Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuemeng Song.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, J., Song, X., Gan, T. et al. PaintNet: A shape-constrained generative framework for generating clothing from fashion model. Multimed Tools Appl 80, 17183–17203 (2021). https://doi.org/10.1007/s11042-020-09009-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09009-y

Keywords

Search

Navigation