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The dominant paradigm in 3D human pose estimation that lifts a 2D posesequence to 3D heavily relies on long-term temporal clues (i.e., using adaunting number of video frames) for improved accuracy, which incursperformance saturation, intractable computation and the non-causal problem.This can be attributed to their inherent inability to perceive spatial contextas plain 2D joint coordinates carry no visual cues. To address this issue, wepropose a straightforward yet powerful solution: leveraging the readilyavailable intermediate visual representations produced by off-the-shelf(pre-trained) 2D pose detectors -- no finetuning on the 3D task is even needed.The key observation is that, while the pose detector learns to localize 2Djoints, such representations (e.g., feature maps) implicitly encode thejoint-centric spatial context thanks to the regional operations in backbonenetworks. We design a simple baseline named Context-Aware PoseFormer toshowcase its effectiveness. Without access to any temporal information, theproposed method significantly outperforms its context-agnostic counterpart,PoseFormer, and other state-of-the-art methods using up to hundreds of videoframes regarding both speed and precision. Project page:https://qitaozhao.github.io/ContextAware-PoseFormer

A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation