L2A: Learning to Accumulate Pose History for Accurate 3D Human Pose Estimation

ArXi:2605.08806v1 Announce Type: new Existing 2D-3D lifting human pose estimation methods have achieved strong performance. But the utilization of historical pose representations across network depth was overlooked. In current pipelines, information is propagated through fixed residual connections, which restricts effective reuse of early-layer features such as fine-grained spatial structures and short-term motion cues. However, naively incorporating historical features across layers is non-trivial.