SHIC-XE: Viewpoint-Invariant Explainability via Dense 2D-3D Correspondences: an Application to Equine Pain Recognition

Traditional explainability methods use 2D visualization techniques such as saliency maps. However, when applied to video data, subject and camera motion produce unstable, flickering maps that cannot be temporally aggregated in meaningful ways. This limitation is critical in medical and veterinary settings, which demand biologically grounded explanations intuitive for experts. One such domain is equine pain assessment. Horses are particularly challenging in the context of pain as they are known to hide pain signals in human presence. This leads to increased interest in automation of equine pain recognition which is explainable, i.e., highlighting informative facial and postural cues. However, current explainability approaches are inadequate for providing temporally consistent and clinically interpretable explanations. To address this gap, we present SHIC-XE, an explainability framework that leverages dense 2D-3D correspondence methods to address fundamental viewpoint-dependency limitations for dynamic scenes. Building upon the existing SHIC correspondence framework, our approach projects neural network attention onto canonical 3D surface prototypes, enabling anatomically-consistent, pose-invariant explanations across temporal sequences. Our primary contribution is establishing quantitative metrics for spatially-consistent explainability evaluation, transforming subjective visualization assessment into statistically analyzable measurements. Unlike existing approaches requiring n separate classifiers for n anatomical regions, our unified correspondence mapping maintains constant architectural complexity while achieving comparable classification performance. We provide a preliminary validation of the framework on equine pain recognition as a challenging testbed, achieving video-level F1 scores of 0.67, 0.80, and 0.70 across three datasets under leave-one-subject-out validation. Our systematic evaluation against expert pain ratings by facial region, using a validated equine pain scale, reveals significant correlations between model attention and expert pain scoring, (ears: r = 0.30, p<0.001; cheek muscles: r = 0.25, p<0.01; eyes: r = −0.19, p<0.05) providing the first quantitative validation of explainability methods against domain expertise in this context. This finding challenges widespread literature claims of clinical relevance based solely on qualitative saliency inspection, establishing a methodological framework for rigorous explainability validation. The correspondence-based approach generalizes beyond the demonstrated application to any domain requiring spatial consistency between 2D observations and 3D structural models, providing a foundational methodology for next-generation explainable AI systems.