_:n2dc3a6bc540845f1bb519fc70e49d65bxb28
<http://schema.org/description>
"Transformer-based approaches have been successfully proposed for 3D human\npose estimation (HPE) from 2D pose sequence and achieved state-of-the-art\n(SOTA) performance. However, current SOTAs have difficulties in modeling\nspatial-temporal correlations of joints at different levels simultaneously.\nThis is due to the poses' spatial-temporal complexity. Poses move at various\nspeeds temporarily with various joints and body-parts movement spatially.\nHence, a cookie-cutter transformer is non-adaptable and can hardly meet the\n\"in-the-wild\" requirement. To mitigate this issue, we propose Hierarchical\nSpatial-Temporal transFormers (HSTFormer) to capture multi-level joints'\nspatial-temporal correlations from local to global gradually for accurate 3D\nHPE. HSTFormer consists of four transformer encoders (TEs) and a fusion module.\nTo the best of our knowledge, HSTFormer is the first to study hierarchical TEs\nwith multi-level fusion. Extensive experiments on three datasets (i.e.,\nHuman3.6M, MPI-INF-3DHP, and HumanEva) demonstrate that HSTFormer achieves\ncompetitive and consistent performance on benchmarks with various scales and\ndifficulties. Specifically, it surpasses recent SOTAs on the challenging\nMPI-INF-3DHP dataset and small-scale HumanEva dataset, with a highly\ngeneralized systematic approach. The code is available at:\nhttps://github.com/qianxiaoye825/HSTFormer."
<https://cdnjs.deepai.org/profile/xiaoye-qian>