Promptable Animal Pose Tracking Across Species
Leveraging foundation vision models for cross-species animal pose tracking, achieving high accuracy with limited labels.
Key Findings
Methodology
This work introduces two approaches based on foundation models: an unsupervised method using multi-modal features for training-free correspondence matching, and a supervised method employing a keypoint prompt encoder to explicitly incorporate structural priors into feature matching. Both utilize large-scale pretrained models like DINOv3 and Diffusion Hyperfeatures. The unsupervised pathway emphasizes robustness across species and environments, relying on nearest-neighbor matching with optional bounding-box constraints. The supervised pathway enhances accuracy by integrating keypoint heatmaps into the feature space, fine-tuning a cross-frame matcher. These methods are evaluated on APTv2 and TigDog datasets, demonstrating a balance of accuracy and generalization.
Key Results
- On APTv2, the unsupervised model achieves up to 86.6% [email protected], surpassing many traditional methods and enabling zero-shot cross-species tracking.
- The supervised model outperforms baseline methods with 84.0% [email protected], showing significant improvements in keypoint localization accuracy.
- Across challenging scenarios, such as long sequences and cluttered backgrounds, the models maintain stable performance, especially on distinctive species like giraffes and zebras, though distal limb points remain difficult due to motion and occlusion.
Significance
This research addresses the critical bottleneck of data annotation in animal pose tracking by leveraging large pretrained models for flexible, low-label, cross-species applications. It offers a scalable, practical solution for wildlife monitoring, behavioral studies, and conservation efforts, reducing costs and increasing deployment potential. The approach bridges the gap between deep learning advances and real-world ecological needs, facilitating broader adoption and enabling large-scale, automated animal behavior analysis.
Technical Contribution
The paper introduces a novel promptable tracking framework that combines foundation model features with explicit keypoint prompts, enabling both training-free and fine-tuned high-precision tracking. The integration of a keypoint prompt encoder and a dense correspondence matcher represents a significant step beyond traditional supervised keypoint detectors, providing robustness and flexibility. The approach exploits the rich semantic and spatial information encoded in large-scale pretrained models, offering a new paradigm for animal pose analysis that minimizes annotation requirements while maximizing generalization.
Novelty
This is the first work to directly utilize large-scale vision foundation models for animal pose tracking, especially in a promptable, cross-species setting. The combination of unsupervised dense feature matching with explicit keypoint priors introduces a new way to achieve robust, generalizable animal pose tracking without extensive task-specific training. Unlike prior methods relying on fixed skeletal models or extensive annotations, this approach leverages the inherent generalization capacity of foundation models, marking a significant innovation in the field.
Limitations
- The models struggle in scenarios involving rapid motion, heavy occlusion, or complex interactions, where feature ambiguity leads to drift or mismatches.
- Distal limb keypoints are less reliably tracked due to subtle movements and appearance ambiguity, especially in cluttered or low-resolution scenes.
- Dependence on accurate bounding box detection affects performance; errors in instance localization can propagate into keypoint tracking. Future work should incorporate temporal cues and adaptive boundary refinement.
Future Work
Future directions include integrating temporal consistency constraints, exploring multi-modal data (e.g., depth, infrared), and developing adaptive bounding box refinement techniques. Extending the framework to multi-animal scenarios with instance-aware tracking and real-time deployment will further enhance practical utility. Additionally, leveraging self-supervised learning to improve feature robustness in challenging environments remains a promising avenue.
AI Executive Summary
Animal pose estimation and tracking are vital for ecological research, wildlife conservation, and behavioral analysis. Traditional methods rely heavily on extensive manual annotation and species-specific models, limiting their scalability and adaptability across diverse environments. This paper introduces a novel framework that leverages large-scale foundation vision models, such as DINOv3 and Diffusion Hyperfeatures, to enable flexible, cross-species animal pose tracking with minimal supervision.
The core innovation lies in combining an unsupervised dense feature matching approach with a supervised pathway that explicitly encodes structural priors via keypoint prompts. The unsupervised method employs a training-free nearest-neighbor matching mechanism, enhanced with bounding-box constraints to mitigate drift, achieving robust cross-species correspondence. The supervised approach fine-tunes a dense cross-frame matcher with a keypoint prompt encoder, significantly boosting accuracy.
Extensive experiments on the challenging APTv2 and TigDog datasets demonstrate the effectiveness of the proposed methods. The unsupervised model achieves up to 86.6% [email protected], outperforming many traditional trackers and enabling zero-shot generalization across species. The supervised model further improves localization precision, reaching 84% [email protected]. These results highlight the potential of foundation models to revolutionize animal behavior analysis, reducing annotation costs while maintaining high accuracy.
This work paves the way for scalable, real-world animal monitoring solutions. Its flexibility allows researchers to specify only a few keypoints of interest, facilitating applications from wildlife surveillance to ecological studies. Despite current limitations in handling rapid movements and occlusions, future work will focus on integrating temporal cues, multi-modal data, and instance-aware multi-animal tracking, promising a comprehensive, efficient approach to animal pose analysis at scale.
Deep Dive
Glossary
Foundation Model (基础模型)
大规模预训练的深度学习模型，具有强泛化能力，能在多任务中表现优异。In this paper, used for extracting rich visual features for correspondence matching.
作为特征提取和匹配的基础，用于跨物种动物姿态追踪。
PCK (关键点正确率)
衡量关键点定位精度的指标，定义为预测点与真实点的距离在归一化阈值内的比例。In this paper, [email protected]表示距离小于10%的图像宽度的预测比例。
评估动物关键点追踪性能的重要指标。
关键点提示编码器
将关键点位置转换为结构化提示（如热图），以引导模型关注特定区域。In this paper,用于增强特征匹配的结构信息。
在有监督路径中显式融入动物结构信息。
Dense correspondence matching (密集对应匹配)
在图像或视频帧之间建立像素级别的对应关系。In this paper,基于基础模型特征实现跨帧匹配。
实现动物关键点在不同帧间的追踪。
零标注 (Zero-shot)
模型无需针对特定任务进行训练即可应用。In this paper,指无需动物标注即可实现跨物种追踪。
极大降低标注成本，提升模型泛化能力。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在极端运动和遮挡场景下的鲁棒性，仍是未来研究的重点。现有基础特征在复杂动态环境中表现有限，需结合时序信息或多模态数据增强。
- 2 多动物同时出现时，实例区分和多目标追踪仍面临挑战，尤其在相似外观和交互复杂的场景中，需开发更有效的实例级匹配机制。
Abstract
Animal pose estimation and tracking is important for wildlife monitoring and conservation research, and with limited expert time for labelling automated approaches are imperative. While human pose estimation and tracking has seen rapid progress thanks to large annotated datasets, animal pose remain challenging, due to large morphological and behavioural differences between species and limited annotated data. Existing approaches either optimise generic keypoint localisation from annotated datasets (such as APTv2) with poor generalisation, or track custom keypoints using visual tracking, at the cost of performance. In this paper, we demonstrate that vision foundation models trained on large datasets can be used effectively to track animal pose with limited labelled data. We propose two models, one unsupervised and the other supervised, to track user-selected keypoints in videos. The supervised approach delivers superior tracking accuracy by employing a keypoint prompt encoder to explicitly inject structural priors from a reference frame into feature matching. In parallel, the unsupervised route provides strong cross-species robustness by leveraging diverse foundation-model features for training-free correspondence matching. Extensive evaluation on challenging animal video benchmarks APTv2 and TigDog demonstrates that our framework achieves strong performance while maintaining an effective balance between accuracy and generalisation, offering a practical solution for real-world animal behaviour analysis and conservation applications.