# Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data

arXiv: https://arxiv.org/abs/2307.06737 · HTML: https://mlsanity.com/paper/2307.06737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 27 of 27)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [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)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [Pose2Seg: Detection Free Human Instance Segmentation](https://mlsanity.com/paper/1803.10683/) (2018)
- [Synthesizing Images of Humans in Unseen Poses](https://mlsanity.com/paper/1804.07739/) (2018)
- [Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields](https://mlsanity.com/paper/2207.13807/) (2022)
- [The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation](https://mlsanity.com/paper/1911.07524/) (2019)
- [Synthetic Humans for Action Recognition from Unseen Viewpoints](https://mlsanity.com/paper/1912.04070/) (2019)
- [Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation](https://mlsanity.com/paper/1805.09707/) (2018)
- [Polarized Self-Attention: Towards High-quality Pixel-wise Regression](https://mlsanity.com/paper/2107.00782/) (2021)
- [Adversarial Semantic Data Augmentation for Human Pose Estimation](https://mlsanity.com/paper/2008.00697/) (2020)
- [Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games](https://mlsanity.com/paper/2107.05617/) (2021)
- [Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module](https://mlsanity.com/paper/2303.04346/) (2023)
- [Unpaired Pose Guided Human Image Generation](https://mlsanity.com/paper/1901.02284/) (2019)
- [I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/2206.10892/) (2022)
- [Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation](https://mlsanity.com/paper/2210.03686/) (2022)
- [Human Pose Estimation in Monocular Omnidirectional Top-View Images](https://mlsanity.com/paper/2304.08186/) (2023)
- [Synthesizing Anyone, Anywhere, in Any Pose](https://mlsanity.com/paper/2304.03164/) (2023)

## Built on by (most cited first, 2 of 2)

- [X as Supervision: Contending with Depth Ambiguity in Unsupervised Monocular 3D Pose Estimation](https://mlsanity.com/paper/2411.13026/) (2024)
- [Human Pose Estimation in Trampoline Gymnastics: How to Improve Performance on Extreme Poses](https://mlsanity.com/paper/2604.01322/) (2026)

## Abstract (arXiv)

Methods and datasets for human pose estimation focus predominantly on side- and front-view scenarios. We overcome the limitation by leveraging synthetic data and introduce RePoGen (RarE POses GENerator), an SMPL-based method for generating synthetic humans with comprehensive control over pose and view. Experiments on top-view datasets and a new dataset of real images with diverse poses show that adding the RePoGen data to the COCO dataset outperforms previous approaches to top- and bottom-view pose estimation without harming performance on common views. An ablation study shows that anatomical plausibility, a property prior research focused on, is not a prerequisite for effective performance. The introduced dataset and the corresponding code are available on https://mirapurkrabek.github.io/RePoGen-paper/ .