# A Novel Dataset for Keypoint Detection of quadruped Animals from Images

arXiv: https://arxiv.org/abs/2108.13958 · HTML: https://mlsanity.com/paper/2108.13958/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation](https://mlsanity.com/paper/1808.05942/) (2018)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning](https://mlsanity.com/paper/1802.09232/) (2018)
- [Dense Pose Transfer](https://mlsanity.com/paper/1809.01995/) (2018)
- [Unsupervised Discovery of Object Landmarks as Structural Representations](https://mlsanity.com/paper/1804.04412/) (2018)
- [Unsupervised learning of object landmarks by factorized spatial embeddings](https://mlsanity.com/paper/1705.02193/) (2017)
- [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)
- [Interspecies Knowledge Transfer for Facial Keypoint Detection](https://mlsanity.com/paper/1704.04023/) (2017)
- [Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features](https://mlsanity.com/paper/1509.04954/) (2015)
- [GoodPoint: unsupervised learning of keypoint detection and description](https://mlsanity.com/paper/2006.01030/) (2020)

## Built on by (most cited first, 19 of 19)

- [Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence](https://mlsanity.com/paper/2311.17034/) (2023)
- [SuperAnimal pretrained pose estimation models for behavioral analysis](https://mlsanity.com/paper/2203.07436/) (2022)
- [Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps](https://mlsanity.com/paper/2312.13216/) (2023)
- [Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species](https://mlsanity.com/paper/2112.06183/) (2021)
- [AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer](https://mlsanity.com/paper/2412.00837/) (2024)
- [Demystifying Unsupervised Semantic Correspondence Estimation](https://mlsanity.com/paper/2207.05054/) (2022)
- [From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection](https://mlsanity.com/paper/2304.03140/) (2023)
- [OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection](https://mlsanity.com/paper/2409.19899/) (2024)
- [AniMer+: Unified Pose and Shape Estimation Across Mammalia and Aves via Family-Aware Transformer](https://mlsanity.com/paper/2508.00298/) (2025)
- [CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming](https://mlsanity.com/paper/2312.08764/) (2023)
- [CVB: A Video Dataset of Cattle Visual Behaviors](https://mlsanity.com/paper/2305.16555/) (2023)
- [Few-shot Shape Recognition by Learning Deep Shape-aware Features](https://mlsanity.com/paper/2312.01315/) (2023)
- [SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild](https://mlsanity.com/paper/2605.07604/) (2026)
- [C3DAG: Controlled 3D Animal Generation using 3D pose guidance](https://mlsanity.com/paper/2406.07742/) (2024)
- [YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals](https://mlsanity.com/paper/2406.16273/) (2024)
- [PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving](https://mlsanity.com/paper/2603.23215/) (2026)
- [GKDT: General Keypoint Detection Transformer](https://mlsanity.com/paper/2607.00752/) (2026)
- [ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals](https://mlsanity.com/paper/2609.37986/) (2026)
- [Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior](https://mlsanity.com/paper/2610.16988/) (2026)

## Abstract (arXiv)

In this paper, we studied the problem of localizing a generic set of keypoints across multiple quadruped or four-legged animal species from images. Due to the lack of large scale animal keypoint dataset with ground truth annotations, we developed a novel dataset, AwA Pose, for keypoint detection of quadruped animals from images. Our dataset contains significantly more keypoints per animal and has much more diverse animals than the existing datasets for animal keypoint detection. We benchmarked the dataset with a state-of-the-art deep learning model for different keypoint detection tasks, including both seen and unseen animal cases. Experimental results showed the effectiveness of the dataset. We believe that this dataset will help the computer vision community in the design and evaluation of improved models for the generalized quadruped animal keypoint detection problem.