BehaviorScope-X: reusing pose-trained visual representations for full-video ethology

Pose-estimation pipelines usually export keypoint coordinates and discard the intermediate visual representations learned to localize animals in a specific assay. We asked whether those discarded representations can be reused for full-video ethology. BehaviorScope-X tests this idea by treating a trained pose checkpoint as both a keypoint estimator and a reusable visual encoder: the pose model is run once to cache detections, keypoints, pose-derived social geometry, and frozen intermediate descriptors, after which compact temporal classifiers are trained on cached multimodal windows. Across MARS resident-intruder videos, cached pose-trained descriptors and pose-derived geometry provided complementary evidence for behavior decoding, recovering sustained behavioral episodes and local sequence structure while revealing a main limitation in dense short-bout regions. The same cache-and-classify design generalized across pose routes, including a MobileNetV3 backbone and a DeepLabCut SuperAnimal HRNet-W32 checkpoint, showing that standard pose workflows can expose behavior-relevant visual descriptors without giving up their keypoint-estimation role. We further tested the approach in Fly-v-Fly aggression, extending the analysis to a second species and shorter behavioral time scale, where sub-second events and annotation-boundary uncertainty limited strict bout recovery. End-to-end profiling showed that the workflow can operate near real time or in real time on consumer hardware. Together, these experiments support amortized pose vision as a practical strategy for reusing assay-trained pose models as stable sources of visual and geometric evidence for scalable behavioral analysis.