FFMN: Fast Fitting Mesh Network for Monocular 3D Human Reconstruction in Live-Line Work Scenarios

In live-line power distribution operations, 3D pose and action recognition of workers holds critical significance for safety assurance and intelligent monitoring. We propose a novel neural network architecture for fast fitting-based parametric 3D human reconstruction (FFMN) from monocular images in live-line work scenarios. FFMN employs convolutional neural networks to extract feature information from input images and adopts an optimization strategy for inverse problems by reprojecting keypoints from the human model onto feature maps to acquire feedback. A transformer-based updater module then adjusts the model to better align with the person in the image. Unlike conventional regression or recurrent models, FFMN trains faster, utilizes fewer parameters, and achieves shorter inference times. Moreover, our FFMN demonstrates a significant inference speed advantage (latency = 15 ms) on the 3DPW and Human3.6M datasets while maintaining competitive accuracy (MPJPE < 50 mm), highlighting its high practicability in real-world applications.