# Deep Human Parsing with Active Template Regression

arXiv: https://arxiv.org/abs/1503.02391 · HTML: https://mlsanity.com/paper/1503.02391/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2015.

## Built on by (most cited first, 30 of 51)

- [StyleGAN-Human: A Data-Centric Odyssey of Human Generation](https://mlsanity.com/paper/2204.11823/) (2022)
- [TeCH: Text-guided Reconstruction of Lifelike Clothed Humans](https://mlsanity.com/paper/2308.08545/) (2023)
- [CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2407.15886/) (2024)
- [EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model](https://mlsanity.com/paper/2406.20076/) (2024)
- [Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing](https://mlsanity.com/paper/2104.07021/) (2021)
- [Multimodal Image Synthesis and Editing: The Generative AI Era](https://mlsanity.com/paper/2112.13592/) (2021)
- [One-shot Implicit Animatable Avatars with Model-based Priors](https://mlsanity.com/paper/2212.02469/) (2022)
- [Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2107.11522/) (2021)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors](https://mlsanity.com/paper/2411.07472/) (2024)
- [Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles](https://mlsanity.com/paper/2103.14098/) (2021)
- [Deep Image Synthesis from Intuitive User Input: A Review and Perspectives](https://mlsanity.com/paper/2107.04240/) (2021)
- [BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification](https://mlsanity.com/paper/2309.04675/) (2023)
- [Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images](https://mlsanity.com/paper/2311.02892/) (2023)
- [Explore Human Parsing Modality for Action Recognition](https://mlsanity.com/paper/2401.02138/) (2024)
- [From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images](https://mlsanity.com/paper/2102.01690/) (2021)
- [Human-centric Relation Segmentation: Dataset and Solution](https://mlsanity.com/paper/2105.11168/) (2021)
- [Tracking by Joint Local and Global Search: A Target-aware Attention based Approach](https://mlsanity.com/paper/2106.04840/) (2021)
- [PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition](https://mlsanity.com/paper/2407.10918/) (2024)
- [Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer](https://mlsanity.com/paper/2101.10620/) (2021)
- [3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective](https://mlsanity.com/paper/2204.13096/) (2022)
- [Open-Vocabulary Semantic Part Segmentation of 3D Human](https://mlsanity.com/paper/2502.19782/) (2025)
- [End-to-end One-shot Human Parsing](https://mlsanity.com/paper/2105.01241/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing](https://mlsanity.com/paper/2111.12346/) (2021)
- [Learning Part Segmentation from Synthetic Animals](https://mlsanity.com/paper/2311.18661/) (2023)
- [The Arm-Swing Is Discriminative in Video Gait Recognition for Athlete Re-Identification](https://mlsanity.com/paper/2106.11280/) (2021)
- [Describe me if you can! Characterized Instance-level Human Parsing](https://mlsanity.com/paper/2201.09594/) (2022)
- [They're All Doctors: Synthesizing Diverse Counterfactuals to Mitigate Associative Bias](https://mlsanity.com/paper/2406.11331/) (2024)
- [Spatial Transformation for Image Composition via Correspondence Learning](https://mlsanity.com/paper/2207.02398/) (2022)

## Abstract (arXiv)

In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an Active Template Regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches 64.38% by our ATR framework, significantly higher than 44.76% based on the state-of-the-art algorithm.