mlsanityData to 7 Oct 2026

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

arXiv 2410.09312 ↗First posted 12 Oct 2024cs.CV cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 76 of its 174 references that are in the map. First posted on arXiv 12 Oct 2024.

Builds on

76 of 174 references.

References · 76

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38 more
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50 most cited of 76 All 76 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Articulate3D: Zero-Shot Text-Driven 3D Object Posingcited by 1
  2. 2026Promptable Animal Pose Tracking Across Speciescited by 0