ViBE: Dressing for Diverse Body Shapes

CVPR, pp. 11056-11066, 2020.

被引用0|引用|浏览53|DOI:https://doi.org/10.1109/CVPR42600.2020.01107
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We explored clothing recommendations that complement an individual’s body shape

摘要

Body shape plays an important role in determining what garments will best suit a given person, yet today\u0027s clothing recommendation methods take a \"one shape fits all\" approach. These body-agnostic vision methods and datasets are a barrier to inclusion, ill-equipped to provide good suggestions for diverse body shapes. We introduce V...更多

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简介
  • Exciting recent advances can link street photos to catalogs [47,54], recommend garments to complete a look [25,33,34,40,73,76], discover styles and trends [3, 32, 57], and search based on subtle visual properties [22, 46]
  • All such directions promise to augment and accelerate the clothing shopping experience, providing consumers with personalized recommendations and putting a content-based index of products at their fingertips.
  • Current largescale datasets are heavily biased to a narrow set of body shapes1—typically thin and tall, owing to the fashionista or celebrity photos from which they are drawn [26, 51, 55, 66, 1not to mention skin tone, age, gender, and other demographic factors
重点内容
  • Research in computer vision is poised to transform the world of consumer fashion
  • Current largescale datasets are heavily biased to a narrow set of body shapes1—typically thin and tall, owing to the fashionista or celebrity photos from which they are drawn [26, 51, 55, 66, 1not to mention skin tone, age, gender, and other demographic factors
  • We propose ViBE, a VIsual Body-aware Embedding that captures clothing’s affinity with different body shapes
  • We explore a new rich online catalog dataset comprised of models of diverse body shape
  • We explored clothing recommendations that complement an individual’s body shape
  • We identified a novel source of Web photo data containing fashion models of diverse body shapes, and developed a body-aware embedding to capture clothing’s affinity with different bodies
方法
  • All methods perform better on dresses than tops
  • This may be due to the fact that dresses cover a larger portion of the body, and could be inherently more selective about which bodies are suitable.
  • The more selective or body-specific a garment is, the more value a body-aware recommendation system can offer; the more body-versatile a garment is, the less impact an intelligent recommendation can have.
  • As the authors focus on the body-specific garments the body-aware embedding’s gain
结论
  • The authors explored clothing recommendations that complement an individual’s body shape.
  • The authors identified a novel source of Web photo data containing fashion models of diverse body shapes, and developed a body-aware embedding to capture clothing’s affinity with different bodies.
  • Through quantitative measurements and human judgments, the authors verified the model’s effectiveness over body-agnostic models, the status quo in the literature.
  • The authors plan to incorporate the body-aware embedding to address fashion styling and compatibility tasks
总结
  • Introduction:

    Exciting recent advances can link street photos to catalogs [47,54], recommend garments to complete a look [25,33,34,40,73,76], discover styles and trends [3, 32, 57], and search based on subtle visual properties [22, 46]
  • All such directions promise to augment and accelerate the clothing shopping experience, providing consumers with personalized recommendations and putting a content-based index of products at their fingertips.
  • Current largescale datasets are heavily biased to a narrow set of body shapes1—typically thin and tall, owing to the fashionista or celebrity photos from which they are drawn [26, 51, 55, 66, 1not to mention skin tone, age, gender, and other demographic factors
  • Methods:

    All methods perform better on dresses than tops
  • This may be due to the fact that dresses cover a larger portion of the body, and could be inherently more selective about which bodies are suitable.
  • The more selective or body-specific a garment is, the more value a body-aware recommendation system can offer; the more body-versatile a garment is, the less impact an intelligent recommendation can have.
  • As the authors focus on the body-specific garments the body-aware embedding’s gain
  • Conclusion:

    The authors explored clothing recommendations that complement an individual’s body shape.
  • The authors identified a novel source of Web photo data containing fashion models of diverse body shapes, and developed a body-aware embedding to capture clothing’s affinity with different bodies.
  • Through quantitative measurements and human judgments, the authors verified the model’s effectiveness over body-agnostic models, the status quo in the literature.
  • The authors plan to incorporate the body-aware embedding to address fashion styling and compatibility tasks
表格
  • Table1: Dataset statistics: number of garments and fashion models for each clustered type
  • Table2: Recommendation AUC on unseen people paired with garments sampled from the entire dataset, where ground-truth labels are provided by human judges. Consistent with Fig. 7a, the proposed model outperforms all the baselines
Download tables as Excel
相关工作
  • Fashion styles and compatibility Early work on computer vision for fashion addresses recognition problems, like matching items seen on the street to a catalog [47, 54], searching for products [22, 46, 86], or parsing an outfit into garments [17, 51, 83, 87]. Beyond recognition, recent work explores models for compatibility that score garments for their mutual affinity [24,33,34,36,73,76,77]. Styles—metapatterns in what people wear—can be learned from images, often with visual attributes [?,3,32,43,57], and Web photos with timestamps and social media “likes” can help model the relative popularity of trends [50, 74]. Unlike our approach, none of the above models account for the influence of body shape on garment compatibility or style.

    Fashion image datasets Celebrities [30, 51], fashionista social media influencers [43, 52, 74, 83, 84], and catalog models [18, 26, 55, 66] are all natural sources of data for computer vision datasets studying fashion. However, these sources inject bias into the body shapes (and other demographics) represented, which can be useful for some applications but limiting for others. Some recent dataset efforts leverage social media and photo sharing platforms like Instagram and Flickr which may access a more inclusive sample of people [42, 57], but their results do not address body shape. We explore a new rich online catalog dataset comprised of models of diverse body shape.
基金
  • UT Austin is supported in part by NSF IIS-1514118
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