AG-EgoPose: Spatially Anchored Residual Correction with Action Context for Monocular Egocentric 3D Pose Estimation

Monocular egocentric 3D pose estimation is difficult because severe foreshortening, self-occlusion, and a restricted field of view often remove the image evidence needed to recover the camera wearer's body. Temporal context can resolve these ambiguities, but unconstrained fusion may also corrupt joints that are already localized reliably from the current frame. We present AG-EgoPose, an action-guided framework that treats temporal information as a bounded-gate residual correction to a spatial pose estimate. A frozen heatmap network produces joint heatmaps and feature-pyramid features, from which a spatial transformer estimates an independently supervised 3D pose. The heatmaps further guide joint-local pooling of frozen DINOv2 patch tokens, while DINOv2 frame tokens are processed by an Ego4D-pretrained ActionFormer to obtain action-scale temporal context. Each spatial joint token, augmented with its local appearance feature, attends to the temporal sequence and predicts a zero-initialized 3D residual. A per-joint gate, derived from heatmap location and confidence statistics, bounds the residual weight. AG-EgoPose improves over the strongest evaluated baselines by 10.1% PA-MPJPE on EgoPW, and by 9.0% MPJPE and 6.3% PA-MPJPE on SceneEgo after fine-tuning. Code will be made publicly available at https://github.com/