SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation

arXiv:2404.15276v1 Announce Type: cross
Abstract: 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 …

arxiv cs.ai cs.cv cs.gr cs.lg cs.mm human transformers type