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Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis

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arxiv 2410.09312 v2 pith:NMGX6UAF submitted 2024-10-12 cs.CV cs.AIcs.LG

Qianyi Deng , Oishi Deb , Amir Patel , Christian Rupprecht , Philip Torr , Niki Trigoni , Andrew Markham This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords animalestimationposeanalysishumanlearningmodalityprovided
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Animal pose estimation (APE) aims to locate the animal body parts using a diverse array of sensor and modality inputs (e.g. RGB cameras, LiDAR, infrared, IMU, acoustic and language cues), which is crucial for research across neuroscience, biomechanics, and veterinary medicine. By evaluating 176 papers since 2011, APE methods are categorised by their input sensor and modality types, output forms, learning paradigms, experimental setup, and application domains, presenting detailed analyses of current trends, challenges, and future directions in single- and multi-modality APE systems. The analysis also highlights the transition between human and animal pose estimation, and how innovations in APE can reciprocally enrich human pose estimation and the broader machine learning paradigm. Additionally, 2D and 3D APE datasets and evaluation metrics based on different sensors and modalities are provided. A regularly updated project page is provided here: https://github.com/ChennyDeng/MM-APE.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Seeing Isn't Orienting: A Cognitively Informed Hierarchical Benchmark for Object Orientation in MLLMs

    cs.CV 2025-05 unverdicted novelty 7.0 of 10

    DORI shows state-of-the-art multimodal models perform near-random on fine-grained object orientation reasoning despite strong scores on broader spatial benchmarks.

  2. Promptable Animal Pose Tracking Across Species

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A promptable framework using frozen foundation-model features achieves competitive keypoint tracking accuracy and cross-species generalization on animal video benchmarks, with supervised and unsupervised variants.

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