WildProp: Visual Estimation of Wildlife Body Proportions at Scale

Population-level morphometric measurements are central toecological and evolutionary studies, but traditionally require controlledimaging or physical specimen handling, limiting their scalability. Wepresent WildProp, a framework for estimating wildlife body-proportiondistributions directly from large-scale, unconstrained image repositories.We cast morphometric estimation as a retrieval-driven correspondenceproblem: given a single user-annotated canonical image, WildProp per-forms pose-aware retrieval using foundation model features, transferspart endpoints via dense patch-level matching, filters predictions us-ing geometric consistency, and aggregates measurements across retrievedimages to estimate length-ratio distributions. Unlike supervised keypointpipelines, our approach adapts to arbitrary species and user-defined bodyparts without per-species training. Evaluations on three large morpho-metric datasets spanning birds and amphibians show median relative er-rors of 10–20%. We further demonstrate broad applicability through casestudies measuring proportions across diverse taxa, including birds, frogs,insects, and flowers. Ablations show that pose-aware retrieval is criticalfor stable estimation, while robust aggregation mitigates keypoint andpose noise. Our results suggest that carefully filtered 2D correspondencesover web-scale imagery can provide scalable morphometric proxies forcomparative analyses across taxa, geography, and seasonality.