Lifting-based methods for 3D Human Pose Estimation (HPE), which predict 3D poses from detected 2D keypoints, often generalize poorly to new datasets and real-world settings. To address this, we propose AugLift, a simple yet effective reformulation of the standard lifting pipeline that significantly improves generalization performance without requiring additional data collection or sensors. AugLift sparsely enriches the standard input -- the 2D keypoint coordinates (x, y) -- by augmenting it with a keypoint detection confidence score c and a corresponding depth estimate d. These additional signals are computed from the image using off-the-shelf, pre-trained models (e. g. , for monocular depth estimation), thereby inheriting their strong generalization capabilities. Importantly, AugLift serves as a modular add-on and can be readily integrated into existing lifting architectures. Our extensive experiments across four datasets demonstrate that AugLift boosts cross-dataset performance on unseen datasets by an average of 10. 1\%, while also improving in-distribution performance by 4. 0\%. These gains are consistent across various lifting architectures, highlighting the robustness of our method. Our analysis suggests that these sparse, keypoint-aligned cues provide robust frame-level context, offering a practical way to significantly improve the generalization of any lifting-based pose estimation model. Code will be made publicly available.
Warner et al. (Sat,) studied this question.