Densepose inference. We involve human annotators to establish dense correspondences from 2D images to surface-based representations of the human body. . For an overview of what DensePose is and its architecture, see DensePose System Overview. This document provides brief tutorials covering DensePose for inference and training on the DensePose-COCO dataset. This document is a modified version of the detectron/GETTING_STARTED. For each part regressing local coordinates within part. Core] DensePose Estimator Authored by LykosAI Created 2 years ago Updated 7 months ago 33 stars Generating per-pixel classification results for selection of surface part. During inference, our system operates at 25fps on 320x240 images and 4-5fps on 800x1100 images using a GTX1080 graphics card. Exportable DensePose inference using TorchScript This is unofficial inference implementation of DensePose from detectron2 The project is focused on creating simple and TorchScript compilable inference interface for the original pretrained models to free them from the heavy dependency on the detectron2 framework. Figure 2: Left: Inference Time vs DensePose AP, Right: PA-MPJPE vs DensePose AP – for both, top-left is best and radii are proportional to the sizes of the models (MB). qhhp ipkv yjxpcl7 nisnu2 w3vb kn fpqozoq fj ey iedgjal