Animal World: A Cross-Species Dataset for Social Animal Pose Understanding

Animal pose understanding in the wild remains challenging due to large morphological variation, frequent occlusion, and complex social interactions. Reliable 2D pose estimation is not only fundamental to animal image understanding, but also provides an important source of supervision and evaluation for monocular 3D animal reconstruction, where accurate real-world 3D ground truth remains difficult to obtain. Existing animal pose benchmarks, however, are typically limited to particular species, sparse pose definitions, or isolated individuals, leaving robustness of animal pose estimation under diverse natural scenes insufficiently characterised. We introduce Animal World, a scene-centric dataset and benchmark for cross-species 2D animal pose understanding that jointly captures morphological diversity and inter-animal interaction in natural environments. Animal World provides a unified anatomical keypoint taxonomy and large-scale instance-level pose annotations, which enable consistent evaluation across species and socially complex scenes. Building on Animal World, we establish a comprehensive diagnostic benchmark that systematically evaluates the generalisation and robustness of animal pose estimation under species and domain shifts, occlusion, and diverse social configurations. Our extensive evaluations reveal substantial performance variation across species, domains, and interaction contexts, highlighting challenges beyond conventional single-animal benchmarks. We further show that Animal World supervision provides more informative 2D pose cues for downstream 3D animal reconstruction, demonstrating its value beyond 2D pose estimation alone. Animal World provides a unified data and evaluation foundation for advancing robust cross-species animal understanding in complex natural environments.