6 research outputs found
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention
Recently, fully-transformer architectures have replaced the defacto
convolutional architecture for the 3D human pose estimation task. In this paper
we propose \textbf{\textit{ConvFormer}}, a novel convolutional transformer that
leverages a new \textbf{\textit{dynamic multi-headed convolutional
self-attention}} mechanism for monocular 3D human pose estimation. We designed
a spatial and temporal convolutional transformer to comprehensively model human
joint relations within individual frames and globally across the motion
sequence. Moreover, we introduce a novel notion of \textbf{\textit{temporal
joints profile}} for our temporal ConvFormer that fuses complete temporal
information immediately for a local neighborhood of joint features. We have
quantitatively and qualitatively validated our method on three common benchmark
datasets: Human3.6M, MPI-INF-3DHP, and HumanEva. Extensive experiments have
been conducted to identify the optimal hyper-parameter set. These experiments
demonstrated that we achieved a \textbf{significant parameter reduction
relative to prior transformer models} while attaining State-of-the-Art (SOTA)
or near SOTA on all three datasets. Additionally, we achieved SOTA for Protocol
III on H36M for both GT and CPN detection inputs. Finally, we obtained SOTA on
all three metrics for the MPI-INF-3DHP dataset and for all three subjects on
HumanEva under Protocol II
Intelligent Sensors for Human Motion Analysis
The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems