{"ID":5438621,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T04:20:05.427450767Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31125","arxiv_id":"2606.31125","title":"WildProp: Visual Estimation of Wildlife Body Proportions at Scale","abstract":"Population-level morphometric measurements underpin ecological and evolutionary studies but traditionally require controlled imaging or physical specimen handling, limiting scalability. We present WildProp, a training-free framework that estimates wildlife body proportion distributions directly from large-scale, unconstrained image repositories. We cast morphometric estimation as a retrieval-driven correspondence problem: given a single user-annotated canonical image, WildProp performs pose-aware retrieval using foundation model features, transfers part endpoints via dense patch-level matching, filters predictions using geometric consistency, and aggregates measurements across retrieved images to estimate population-level ratio distributions. Unlike supervised keypoint pipelines, our approach adapts to arbitrary species and user-defined parts without per-species training. Evaluations on three large morphometric datasets spanning birds and amphibians show median relative errors of 10-20%. We further highlight the broad applicability of our approach through a number of case studies measuring various proportions across diverse taxa, including birds, frogs, insects, and flowers. Ablations demonstrate that pose-aware retrieval is critical for stable estimation, while robust aggregation mitigates keypoint and pose noise. Our results indicate that carefully curated 2D correspondences over web-scale imagery can provide scalable morphometric proxies for comparative and subgroup analyses across taxa, geography, and seasonality.","short_abstract":"Population-level morphometric measurements underpin ecological and evolutionary studies but traditionally require controlled imaging or physical specimen handling, limiting scalability. We present WildProp, a training-free framework that estimates wildlife body proportion distributions directly from large-scale, uncons...","url_abs":"https://arxiv.org/abs/2606.31125","url_pdf":"https://arxiv.org/pdf/2606.31125v1","authors":"[\"Mustafa Chasmai\",\"Aaron Sun\",\"Subhransu Maji\"]","published":"2026-06-30T04:38:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}