# OpenApePose: a database of annotated ape photographs for pose estimation

arXiv: https://arxiv.org/abs/2212.00741 · HTML: https://mlsanity.com/paper/2212.00741/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [AP-10K: A Benchmark for Animal Pose Estimation in the Wild](https://mlsanity.com/paper/2108.12617/) (2021)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop](https://mlsanity.com/paper/2007.11110/) (2020)
- [Pretraining boosts out-of-domain robustness for pose estimation](https://mlsanity.com/paper/1909.11229/) (2019)
- [Deep learning tools for the measurement of animal behavior in neuroscience](https://mlsanity.com/paper/1909.13868/) (2019)
- [RGBD-Dog: Predicting Canine Pose from RGBD Sensors](https://mlsanity.com/paper/2004.07788/) (2020)
- [AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild](https://mlsanity.com/paper/2103.13282/) (2021)
- [Transferring Dense Pose to Proximal Animal Classes](https://mlsanity.com/paper/2003.00080/) (2020)
- [Visual Recognition of Great Ape Behaviours in the Wild](https://mlsanity.com/paper/2011.10759/) (2020)
- [T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information](https://mlsanity.com/paper/2104.08029/) (2021)
- [Leaving Flatland: Advances in 3D behavioral measurement](https://mlsanity.com/paper/2112.01987/) (2021)
- [The population doctrine in cognitive neuroscience](https://mlsanity.com/paper/2104.00145/) (2021)
- [Self-supervised Secondary Landmark Detection via 3D Representation Learning](https://mlsanity.com/paper/2110.00543/) (2021)
- [The whole prefrontal cortex is premotor cortex](https://mlsanity.com/paper/2106.04651/) (2021)
- [The promise of behavioral tracking systems for advancing primate animal welfare](https://mlsanity.com/paper/2205.13460/) (2022)

## Built on by (most cited first, 10 of 10)

- [ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors](https://mlsanity.com/paper/2310.16447/) (2023)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [Web-Scale Collection of Video Data for 4D Animal Reconstruction](https://mlsanity.com/paper/2511.01169/) (2025)
- [AlphaChimp: Tracking and Behavior Recognition of Chimpanzees](https://mlsanity.com/paper/2410.17136/) (2024)
- [Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis](https://mlsanity.com/paper/2410.09312/) (2024)
- [BigMaQ: A Big Macaque Motion and Animation Dataset Bridging Image and 3D Pose Representations](https://mlsanity.com/paper/2602.19874/) (2026)
- [From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave](https://mlsanity.com/paper/2405.20025/) (2024)
- [A Review on Coarse to Fine-Grained Animal Action Recognition](https://mlsanity.com/paper/2506.01214/) (2025)
- [CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild](https://mlsanity.com/paper/2603.25524/) (2026)
- [BAT3R: Bootstrapping Articulated 3D Reconstruction from 2D Image Collections](https://mlsanity.com/paper/2607.03891/) (2026)

## Abstract (arXiv)

Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose, a new public dataset of 71,868 photographs, annotated with 16 body landmarks, of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans (COCO) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large specialized databases for animal tracking systems and confirm the utility of our new ape database.