  Accurately estimating the 3D pose of humans in video sequences requires both
accuracy and a well-structured architecture. With the success of transformers,
we introduce the Refined Temporal Pyramidal Compression-and-Amplification
(RTPCA) transformer. Exploiting the temporal dimension, RTPCA extends
intra-block temporal modeling via its Temporal Pyramidal
Compression-and-Amplification (TPCA) structure and refines inter-block feature
interaction with a Cross-Layer Refinement (XLR) module. In particular, TPCA
block exploits a temporal pyramid paradigm, reinforcing key and value
representation capabilities and seamlessly extracting spatial semantics from
motion sequences. We stitch these TPCA blocks with XLR that promotes rich
semantic representation through continuous interaction of queries, keys, and
values. This strategy embodies early-stage information with current flows,
addressing typical deficits in detail and stability seen in other
transformer-based methods. We demonstrate the effectiveness of RTPCA by
achieving state-of-the-art results on Human3.6M, HumanEva-I, and MPI-INF-3DHP
benchmarks with minimal computational overhead. The source code is available at
https://github.com/hbing-l/RTPCA.
