# Renovating Parsing R-CNN for Accurate Multiple Human Parsing

arXiv: https://arxiv.org/abs/2009.09447 · HTML: https://mlsanity.com/paper/2009.09447/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 30 of 37)

- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [Animatable Neural Radiance Fields from Monocular RGB Videos](https://mlsanity.com/paper/2106.13629/) (2021)
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- [Controllable Generation with Text-to-Image Diffusion Models: A Survey](https://mlsanity.com/paper/2403.04279/) (2024)
- [DrapeNet: Garment Generation and Self-Supervised Draping](https://mlsanity.com/paper/2211.11277/) (2022)
- [Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation](https://mlsanity.com/paper/2204.04655/) (2022)
- [Learned Vertex Descent: A New Direction for 3D Human Model Fitting](https://mlsanity.com/paper/2205.06254/) (2022)
- [Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing](https://mlsanity.com/paper/2103.04570/) (2021)
- [HAHA: Highly Articulated Gaussian Human Avatars with Textured Mesh Prior](https://mlsanity.com/paper/2404.01053/) (2024)
- [DIG: Draping Implicit Garment over the Human Body](https://mlsanity.com/paper/2209.10845/) (2022)
- [3D Segmentation of Humans in Point Clouds with Synthetic Data](https://mlsanity.com/paper/2212.00786/) (2022)
- [PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation](https://mlsanity.com/paper/2301.00954/) (2023)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition](https://mlsanity.com/paper/2204.04654/) (2022)
- [AIParsing: Anchor-free Instance-level Human Parsing](https://mlsanity.com/paper/2207.06854/) (2022)
- [Quality-Aware Network for Human Parsing](https://mlsanity.com/paper/2103.05997/) (2021)
- [What Decreases Editing Capability? Domain-Specific Hybrid Refinement for Improved GAN Inversion](https://mlsanity.com/paper/2301.12141/) (2023)
- [Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation](https://mlsanity.com/paper/2312.08195/) (2023)
- [Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations](https://mlsanity.com/paper/2406.10114/) (2024)
- [End-to-end One-shot Human Parsing](https://mlsanity.com/paper/2105.01241/) (2021)
- [Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2112.12917/) (2021)
- [LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space](https://mlsanity.com/paper/2209.12746/) (2022)
- [CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models](https://mlsanity.com/paper/2412.19331/) (2024)
- [Quality-Aware Network for Face Parsing](https://mlsanity.com/paper/2106.07368/) (2021)
- [Describe me if you can! Characterized Instance-level Human Parsing](https://mlsanity.com/paper/2201.09594/) (2022)
- [RepParser: End-to-End Multiple Human Parsing with Representative Parts](https://mlsanity.com/paper/2208.12908/) (2022)
- [UniParser: Multi-Human Parsing with Unified Correlation Representation Learning](https://mlsanity.com/paper/2310.08984/) (2023)
- [Single-stage Multi-human Parsing via Point Sets and Center-based Offsets](https://mlsanity.com/paper/2304.11356/) (2023)
- [Hierarchical Pyramid Representations for Semantic Segmentation](https://mlsanity.com/paper/2104.01792/) (2021)
- [SGM-Net: Semantic Guided Matting Net](https://mlsanity.com/paper/2208.07496/) (2022)

## Abstract (arXiv)

Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously. This is a very challenging task due to the diverse human appearance, semantic ambiguity of different body parts, and complex background. Through analysis of multiple human parsing task, we observe that human-centric global perception and accurate instance-level parsing scoring are crucial for obtaining high-quality results. But the most state-of-the-art methods have not paid enough attention to these issues. To reverse this phenomenon, we present Renovating Parsing R-CNN (RP R-CNN), which introduces a global semantic enhanced feature pyramid network and a parsing re-scoring network into the existing high-performance pipeline. The proposed RP R-CNN adopts global semantic representation to enhance multi-scale features for generating human parsing maps, and regresses a confidence score to represent its quality. Extensive experiments show that RP R-CNN performs favorably against state-of-the-art methods on CIHP and MHP-v2 datasets. Code and models are available at https://github.com/soeaver/RP-R-CNN.