  The dominant paradigm in 3D human pose estimation that lifts a 2D pose
sequence to 3D heavily relies on long-term temporal clues (i.e., using a
daunting number of video frames) for improved accuracy, which incurs
performance saturation, intractable computation and the non-causal problem.
This can be attributed to their inherent inability to perceive spatial context
as plain 2D joint coordinates carry no visual cues. To address this issue, we
propose a straightforward yet powerful solution: leveraging the readily
available 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 2D
joints, such representations (e.g., feature maps) implicitly encode the
joint-centric spatial context thanks to the regional operations in backbone
networks. We design a simple baseline named Context-Aware PoseFormer to
showcase its effectiveness. Without access to any temporal information, the
proposed method significantly outperforms its context-agnostic counterpart,
PoseFormer, and other state-of-the-art methods using up to hundreds of video
frames regarding both speed and precision. Project page:
https://qitaozhao.github.io/ContextAware-PoseFormer
