  Existing Transformers for monocular 3D human shape and pose estimation
typically have a quadratic computation and memory complexity with respect to
the feature length, which hinders the exploitation of fine-grained information
in high-resolution features that is beneficial for accurate reconstruction. In
this work, we propose an SMPL-based Transformer framework (SMPLer) to address
this issue. SMPLer incorporates two key ingredients: a decoupled attention
operation and an SMPL-based target representation, which allow effective
utilization of high-resolution features in the Transformer. In addition, based
on these two designs, we also introduce several novel modules including a
multi-scale attention and a joint-aware attention to further boost the
reconstruction performance. Extensive experiments demonstrate the effectiveness
of SMPLer against existing 3D human shape and pose estimation methods both
quantitatively and qualitatively. Notably, the proposed algorithm achieves an
MPJPE of 45.2 mm on the Human3.6M dataset, improving upon Mesh Graphormer by
more than 10% with fewer than one-third of the parameters. Code and pretrained
models are available at https://github.com/xuxy09/SMPLer.
