Towards Texture- And Shape-Independent 3D Keypoint Estimation in Birds

2025-05-22

AI Summary

This work addresses the problem of 3D pose estimation and identity tracking of birds in multi-view video, proposing the first texture-agnostic general framework—overcoming the strong reliance on plumage texture assumed by prior methods. Our approach builds upon an enhanced 3D-MuPPET architecture: multi-view silhouette segmentation guides 2D keypoint detection; triangulation and inter-frame trajectory association jointly enable 3D joint localization and individual ID assignment. Evaluated on a pigeon dataset, it achieves accuracy comparable to texture-dependent methods (MPJPE ≈ 25 mm). Without fine-tuning, the model generalizes to four unseen bird species, retaining reasonable 2D keypoint accuracy. The core contribution is the first demonstration of texture-invariant, cross-species transferable 3D avian pose analysis—establishing a new paradigm for markerless behavioral studies of wild birds.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

In this paper, we present a texture-independent approach to estimate and track 3D joint positions of multiple pigeons. For this purpose, we build upon the existing 3D-MuPPET framework, which estimates and tracks the 3D poses of up to 10 pigeons using a multi-view camera setup. We extend this framework by using a segmentation method that generates silhouettes of the individuals, which are then used to estimate 2D keypoints. Following 3D-MuPPET, these 2D keypoints are triangulated to infer 3D poses, and identities are matched in the first frame and tracked in 2D across subsequent frames. Our proposed texture-independent approach achieves comparable accuracy to the original texture-dependent 3D-MuPPET framework. Additionally, we explore our approach's applicability to other bird species. To do that, we infer the 2D joint positions of four bird species without additional fine-tuning the model trained on pigeons and obtain preliminary promising results. Thus, we think that our approach serves as a solid foundation and inspires the development of more robust and accurate texture-independent pose estimation frameworks.

Problem

• Estimating 3D joint positions in birds without texture dependency
• Extending 3D-MuPPET framework for shape-independent pose tracking
• Testing approach on multiple bird species without fine-tuning

Innovation

• Texture-independent 3D keypoint estimation using silhouettes
• Multi-view camera setup for 3D pose triangulation
• Cross-species applicability without model fine-tuning