# 3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View

arXiv: https://arxiv.org/abs/2008.06133 · HTML: https://mlsanity.com/paper/2008.06133/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 30 of 40)

- [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021)
- [GART: Gaussian Articulated Template Models](https://mlsanity.com/paper/2311.16099/) (2023)
- [LASR: Learning Articulated Shape Reconstruction from a Monocular Video](https://mlsanity.com/paper/2105.02976/) (2021)
- [Reconstructing Animatable Categories from Videos](https://mlsanity.com/paper/2305.06351/) (2023)
- [Birds of a Feather: Capturing Avian Shape Models from Images](https://mlsanity.com/paper/2105.09396/) (2021)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos](https://mlsanity.com/paper/2403.17103/) (2024)
- [hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition](https://mlsanity.com/paper/2106.10102/) (2021)
- [3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture](https://mlsanity.com/paper/2303.13174/) (2023)
- [BAGS: Building Animatable Gaussian Splatting from a Monocular Video with Diffusion Priors](https://mlsanity.com/paper/2403.11427/) (2024)
- [MoDA: Modeling Deformable 3D Objects from Casual Videos](https://mlsanity.com/paper/2304.08279/) (2023)
- [3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking](https://mlsanity.com/paper/2308.15316/) (2023)
- [DreaMo: Articulated 3D Reconstruction From A Single Casual Video](https://mlsanity.com/paper/2312.02617/) (2023)
- [S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video](https://mlsanity.com/paper/2405.12607/) (2024)
- [Generative Zoo](https://mlsanity.com/paper/2412.08101/) (2024)
- [Creative Birds: Self-Supervised Single-View 3D Style Transfer](https://mlsanity.com/paper/2307.14127/) (2023)
- [Learning Implicit Representation for Reconstructing Articulated Objects](https://mlsanity.com/paper/2401.08809/) (2024)
- [Web-Scale Collection of Video Data for 4D Animal Reconstruction](https://mlsanity.com/paper/2511.01169/) (2025)
- [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)
- [Dessie: Disentanglement for Articulated 3D Horse Shape and Pose Estimation from Images](https://mlsanity.com/paper/2410.03438/) (2024)
- [Reconstructing Animals and the Wild](https://mlsanity.com/paper/2411.18807/) (2024)
- [HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis](https://mlsanity.com/paper/2506.19291/) (2025)
- [AniMer+: Unified Pose and Shape Estimation Across Mammalia and Aves via Family-Aware Transformer](https://mlsanity.com/paper/2508.00298/) (2025)
- [Model-based Metric 3D Shape and Motion Reconstruction of Wild Bottlenose Dolphins in Drone-Shot Videos](https://mlsanity.com/paper/2504.15782/) (2025)
- [DogMo: A Large-Scale Multi-View RGB-D Dataset for 4D Canine Motion Recovery](https://mlsanity.com/paper/2510.24117/) (2025)
- [Advances and Trends in the 3D Reconstruction of the Shape and Motion of Animals](https://mlsanity.com/paper/2508.16062/) (2025)
- [Learning-based Monocular 3D Reconstruction of Birds: A Contemporary Survey](https://mlsanity.com/paper/2207.04512/) (2022)
- [CLHOP: Combined Audio-Video Learning for Horse 3D Pose and Shape Estimation](https://mlsanity.com/paper/2407.01244/) (2024)
- [Temporally-consistent 3D Reconstruction of Birds](https://mlsanity.com/paper/2408.13629/) (2024)
- [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)

## Abstract (arXiv)

Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture the unique shape and pose space displayed by live birds. We then introduce a pipeline and experiments for keypoint, mask, pose, and shape regression that recovers accurate avian postures from single views. Finally, we provide extensive multi-view keypoint and mask annotations collected from a group of 15 social birds housed together in an outdoor aviary. The project website with videos, results, code, mesh model, and the Penn Aviary Dataset can be found at https://marcbadger.github.io/avian-mesh.