# Human Body Shape Classification Based on a Single Image

arXiv: https://arxiv.org/abs/2305.18480 · HTML: https://mlsanity.com/paper/2305.18480/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [Tex2Shape: Detailed Full Human Body Geometry From a Single Image](https://mlsanity.com/paper/1904.08645/) (2019)
- [Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation](https://mlsanity.com/paper/1904.10506/) (2019)
- [THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers](https://mlsanity.com/paper/2106.09336/) (2021)
- [Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On](https://mlsanity.com/paper/2009.04592/) (2020)
- [Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping](https://mlsanity.com/paper/1609.02489/) (2016)
- [A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce](https://mlsanity.com/paper/1907.09844/) (2019)
- [Image Based Fashion Product Recommendation with Deep Learning](https://mlsanity.com/paper/1805.08694/) (2018)
- [SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images](https://mlsanity.com/paper/1905.11784/) (2019)
- [Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources](https://mlsanity.com/paper/1807.03235/) (2018)
- [Personalizing Similar Product Recommendations in Fashion E-commerce](https://mlsanity.com/paper/1806.11371/) (2018)
- [An LSTM-Based Dynamic Customer Model for Fashion Recommendation](https://mlsanity.com/paper/1708.07347/) (2017)

## Built on by (most cited first, 1 of 1)

- [DL-EWF: Deep Learning Empowering Women's Fashion with Grounded-Segment-Anything Segmentation for Body Shape Classification](https://mlsanity.com/paper/2404.04891/) (2024)

## Abstract (arXiv)

There is high demand for online fashion recommender systems that incorporate the needs of the consumer's body shape. As such, we present a methodology to classify human body shape from a single image. This is achieved through the use of instance segmentation and keypoint estimation models, trained only on open-source benchmarking datasets. The system is capable of performing in noisy environments owing to to robust background subtraction. The proposed methodology does not require 3D body recreation as a result of classification based on estimated keypoints, nor requires historical information about a user to operate - calculating all required measurements at the point of use. We evaluate our methodology both qualitatively against existing body shape classifiers and quantitatively against a novel dataset of images, which we provide for use to the community. The resultant body shape classification can be utilised in a variety of downstream tasks, such as input to size and fit recommendation or virtual try-on systems.