Autors: Vladimirov, I. H., Nikolova, D. V., Terneva, Z. A.
Title: Overview of Methods for 3D Reconstruction of Human Models with Applications in Fashion E-commerce
Keywords: Overview, 3D Reconstruction, E-commerce, Monocular Video, Im

Abstract: In this scientific paper, an overview of different methodologies and algorithms used for the reconstruction of 3D human models from 2D videos of people in action is presented. These methods could be applicable in the growing e-commerce business. Due to emerging challenges of global warming and the coronavirus pandemic, many developments in the apparel industry must be made in order to make the branch more sustainable and eco-friendly. In this time of globalisation, many brands produce and sell their items internationally and use long-distance shipping to distribute and deliver to their shops and clients. With online shopping, there is a big concern with clothing being the “wrong size” or “wrong item”. To tackle this problem companies, offer their clients the opportunity to return ill-fitting items, but that is a problem in itself because it increases their carbon footprint and it creates unnecessary pollution. A possible solution may come from the use of a computer vision application


  1. BOF, McKinsey Company, 2021, The State of Fashion 2021, Online, BOF
  2. Digital Commerce 360 Company, 2020, 2020 Online Apparel Report, Online, Digital Commerce 360 Company
  3. Kushwah, S.V., Singh, A., 2019, From Traditional Shopping to Online Shopping A Study of the Paradigm Shift in Consumer, Journal of General Management Research, Volume 6(1), pp. pp.1-13
  4. Richter, W., 2020, Online Sales by Category, in Weirdest Economy Ever, Wolf Street Corp, <>, Дата на последен преглед (Last accessed on): 15.09.2022
  5. BOF, 2021, The sustainability gap, Online, BOF
  6. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J., 2016, Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image, Amsterdam, The Netherlands., 11–14 October 2016, <Switzerland>, Springer Cham
  7. Li, S., Chan, A.B., 2014, 3D Human Pose Estimation from Monocular images with Deep Convolutional Neural Network, Singapore, 1-5 November 2014, <Switzerland>, Springer Cham
  8. Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., Fua, P., 2016, Structured prediction of 3d human pose with deep neural networks, York, UK, 19-22 September 2016, <Online>, BMVA Press
  9. Pavlakos, G., Zhou, X., Derpanis, K.G., K.Daniilidis, K.G., 2017, Coarse-to-Fine volumetric prediction for single-image 3d human pose, Honolulu, HI, USA, 21-26 July 2017, <->, IEEE
  10. Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y., 2017, Towards 3d human pose estimation in the wild: a weakly-supervised approach, Venice, Italy, 22-29 October 2017, <Piscataway, NJ>, IEEE
  11. Martinez, J., Hossain, R., Romero, J. Little, J.J., 2017, A simple yet effective baseline for 3d human pose estimation, Venice, Italy, 22-29 October 2017, <Piscataway, NJ>, IEEE
  12. Moreno-Noguer, F., 2017, 3d human pose estimation from a single image via distance matrix regression, Honolulu, HI, USA, 21-26 July 2017, <->, IEEE
  13. Zhou, Q.Y., Koltun, V., 2014, Color map optimization for 3d reconstruction with consumer depth cameras, ACM Transactions on Graphics, Volume 33(4), pp. Article No.:155, pp.1-10
  14. Cui, Y., Chang, W., N’oll, T., Stricker, D., 2012, Kinectavatar: fully automatic body capture using a single kinect, Daejeon, Korea, 5-9 November 2012, <Switzerland>, Springer Cham
  15. Shapiro, A., Feng, A., Wang, R., Li, H., Bolas, M., Medioni, G. and Suma, E., 2014, Rapid avatar capture and simulation using commodity depth sensors”,, Computer Animation and Virtual Worlds, Volume 25(3-4), pp. pp.201-211
  16. Dou, M., Khamis, S., Degtyarev, Y., Davidson, P., Fanello, S.R., Kowdle, A., Escolano, S.O., Rhemann, C., Kim, D., Taylor, J., Kohli, P., Tankovich, V. and Izadi, S., 2016, Fusion4D: real-time performance capture of challenging scenes, ACM Transactions on Graphics, Volume 35(4), pp. Article No.:114, pp.1-13
  17. Leroy, V., Franco, J.S., Boyer, E., 2017, Multi-View DynamicShape Refinement Using Local Temporal Integration, Venice, Italy, 22-29 October 2017, <Piscataway, NJ>, IEEE
  18. Orts-Escolano, S., Rhemann, C., Fanello, S., Chang, W., Kowdle, A., Degtyarev, Y., Kim, D., Davidson, P.L., Khamis, S., Dou, M., Tankovich, V., Loop, C., Cai, Q., Chou, P.A., Mennicken, S., Valentin, J., Pradeep, V., Wang, S., Kang, S.B. and Kohli, P., 2016, Holoportation: Virtual 3d teleportation in real-time, Tokyo, Japan, 16-19 October 2016, <New York, NY, USA>, Association for Computing Machinery
  19. Xu, W., Chatterjee, A., Zollhöfer, M., Rhodin, H., Mehta, D., Seidel, H.-P. and Theobalt, C., 2018, MonoPerfCap: Human Performance Capture From Monocular Video, ACM Transactions on Graphics, Volume 37(2), pp. Article No.:27, pp.1-15
  20. Rhodin, H., Robertini, N., Casas, D., Richardt, C., Seidel, HP., Theobalt, C. (2016). General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues. In: Leibe, B., Matas, J., Sebe, N., Welling, M., 2016, General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues, Amsterdam, The Netherlands, 11-14 October 2016, <Switzerland>, Springer Cham
  21. Alldieck, T., Magnor, M., Bhatnagar, B.L., Theobalt, C., Pons-Moll, G., 2019, Learning to Reconstruct People in Clothing From a Single RGB Camera, Long Beach, CA, USA, 16-20 June 2019, <->, IEEE
  22. Jinka, S.S., Rohan, C., Sharma, A., Narayanan, P., 2020, PeeledHuman: Robust Shape Representation for Textured 3D Human Body Reconstruction, Online, 25-28 November 2020, <->, IEEE
  23. Lassner, C., Romero, J., Kiefel, M., Bogo, F,. Black, M.J., Gehler, P.V., 2017, Unite the people: Closing the loop between 3d and 2d human representations, Honolulu, HI, USA, 21-26 July 2017, <->, IEEE
  24. He, K., Zhang, X., Ren, S., Sun, J., 2016, Deep residual learning for image recognition, Las Vegas, NV, USA, 27-30 June 2016, <->, IEEE
  25. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G., 2018, Video based reconstruction of 3d people models, Salt Lake City, UT, USA, 18-23 June 2018, <->, IEEE
  26. Makarov, I., Chernyshev, D., 2020, Real-Time 3D Model Reconstruction and Mapping for Fashion, Milan, Italy, 7-9 July 2020, <->, IEEE
  27. Kanazawal, A., Black, M.J., Jacobs, D.W., Malik, J., 2018, End-to-end Recovery of Human Shape and Pose, Salt Lake City, UT, USA, 18-23 June 2018, <->, IEEE


ICEST Conference, issue 56, pp. 19 - 22, 2021, Bulgaria, IEEE, DOI 10.1109/ICEST52640.2021.9483564

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Цитирания (Citation/s):
1. Kong, Doyeon. "4D GOLF APPAREL WEAR SIMULATION: REVOLUTIONIZING E-COMMERCE MARKETS." (2022). - 2022 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
2. Mesjar, Lyndsay, et al. "The Intersection of Fashion, Immersive Technology, and Sustainability: A Literature Review." Sustainability 15.4 (2023): 3761. - 2023 - в издания, индексирани в Scopus или Web of Science
3. Nikolova, D., Vladimirov, I., & Manolova, A. (2023, June). An Experimental Analysis of Deep Learning Models for Human Activity Recognition with Synthetic Data. In 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) (pp. 277-280). IEEE. - 2023 - в издания, индексирани в Scopus или Web of Science
4. Kong, D., Seock, Y. K., Marschner, S., & Park, H. T. (2023). Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint. Sustainability, 15(14), 11444. - 2023 - в издания, индексирани в Scopus или Web of Science
5. Vladimirov, I., Nenova, M., Nikolova, D., & Terneva, Z. (2022, June). Security and privacy protection obstacles with 3D reconstructed models of people in applications and the metaverse: A survey. In 2022 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) (pp. 1-4). IEEE. - 2022 - в издания, индексирани в Scopus или Web of Science
6. Goti, A., Querejeta-Lomas, L., Almeida, A., de la Puerta, J. G., & López-de-Ipiña, D. (2023). Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review. Mathematics, 11(13), 2943. - 2023 - в издания, индексирани в Scopus или Web of Science

Вид: постер/презентация в международен форум, публикация в реферирано издание, индексирана в Scopus