# 3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture

arXiv: https://arxiv.org/abs/2303.13174 · HTML: https://mlsanity.com/paper/2303.13174/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 56 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [AP-10K: A Benchmark for Animal Pose Estimation in the Wild](https://mlsanity.com/paper/2108.12617/) (2021)
- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [Creatures great and SMAL: Recovering the shape and motion of animals from video](https://mlsanity.com/paper/1811.05804/) (2018)
- [Deep learning tools for the measurement of animal behavior in neuroscience](https://mlsanity.com/paper/1909.13868/) (2019)
- [3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View](https://mlsanity.com/paper/2008.06133/) (2020)
- [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)
- [Florida Wildlife Camera Trap Dataset](https://mlsanity.com/paper/2106.12628/) (2021)
- [Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary](https://mlsanity.com/paper/2212.00266/) (2022)

## Built on by (most cited first, 18 of 18)

- [BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes](https://mlsanity.com/paper/2411.06896/) (2024)
- [3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking](https://mlsanity.com/paper/2308.15316/) (2023)
- [Web-Scale Collection of Video Data for 4D Animal Reconstruction](https://mlsanity.com/paper/2511.01169/) (2025)
- [The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification](https://mlsanity.com/paper/2511.15622/) (2025)
- [DogMo: A Large-Scale Multi-View RGB-D Dataset for 4D Canine Motion Recovery](https://mlsanity.com/paper/2510.24117/) (2025)
- [Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis](https://mlsanity.com/paper/2410.09312/) (2024)
- [Pose Splatter: A 3D Gaussian Splatting Model for Quantifying Animal Pose and Appearance](https://mlsanity.com/paper/2505.18342/) (2025)
- [Context-Aware Outlier Rejection for Robust Multi-View 3D Tracking of Similar Small Birds in An Outdoor Aviary](https://mlsanity.com/paper/2412.16511/) (2024)
- [Towards Application-Specific Evaluation of Vision Models: Case Studies in Ecology and Biology](https://mlsanity.com/paper/2505.02825/) (2025)
- [Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion](https://mlsanity.com/paper/2512.02017/) (2025)
- [Neural Texture Puppeteer: A Framework for Neural Geometry and Texture Rendering of Articulated Shapes, Enabling Re-Identification at Interactive Speed](https://mlsanity.com/paper/2311.17109/) (2023)
- [8-Calves Image dataset](https://mlsanity.com/paper/2503.13777/) (2025)
- [Towards Texture- And Shape-Independent 3D Keypoint Estimation in Birds](https://mlsanity.com/paper/2505.16633/) (2025)
- [SteerPose: Simultaneous Extrinsic Camera Calibration and Matching from Articulation](https://mlsanity.com/paper/2506.01691/) (2025)
- [Not All Birds Look The Same: Identity-Preserving Generation For Birds](https://mlsanity.com/paper/2512.04485/) (2025)
- [Self-Supervised Animal Identification for Long Videos](https://mlsanity.com/paper/2601.09663/) (2026)
- [WildBox: A Dataset and Benchmark for Aerial Monocular 3D Detection of African Savanna Wildlife](https://mlsanity.com/paper/2606.21309/) (2026)
- [TrackFish3D: Self-Supervised 3D Tracking of Schooling Fish from Multi-view Videos](https://mlsanity.com/paper/2609.38347/) (2026)

## Abstract (arXiv)

Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. However, large datasets of annotated images of animals for markerless pose tracking, especially high-resolution images taken from multiple angles with accurate 3D annotations, are still scant. Here, we propose a method that uses a motion capture (mo-cap) system to obtain a large amount of annotated data on animal movement and posture (2D and 3D) in a semi-automatic manner. Our method is novel in that it extracts the 3D positions of morphological keypoints (e.g eyes, beak, tail) in reference to the positions of markers attached to the animals. Using this method, we obtained, and offer here, a new dataset - 3D-POP with approximately 300k annotated frames (4 million instances) in the form of videos having groups of one to ten freely moving birds from 4 different camera views in a 3.6m x 4.2m area. 3D-POP is the first dataset of flocking birds with accurate keypoint annotations in 2D and 3D along with bounding box and individual identities and will facilitate the development of solutions for problems of 2D to 3D markerless pose, trajectory tracking, and identification in birds.