KTN: Knowledge Transfer Network for Multi-person Densepose Estimation

Introduction

This is the implementation of KTN: Knowledge Transfer Network for Multi-person Densepose Estimation.In this work, we address the multi-person densepose estimation problem, which aims at learning dense correspondences between 2D pixels of human body and 3D surface. It still poses several challenges due to real-world scenes with scale variations and noisy labels with insufficient annotations. In particular, we address two main problems: 1) how to design a simple yet effective pipeline for densepose estimation; and 2) how to equip this pipeline with the ability of handing the issue of noise labels(i.e., limited annotation and class-unbalance). To solve these problems, we develop a novel densepose estimation framework based on the two-stage pipeline, called \textit{Knowledge Transfer Network} (KTN). Unlike existing works which directly propagate the pyramidal base feature among regions, we enhance the representation power of base features through a {\normalsize\bf{multi-instance decoder(MID)}}, which preserves more details of foreground instances while suppresses the activations of backgrounds. Then, we introduce a {\normalsize\bf{knowledge transfer machine(KTM)}}, which estimates densepose through the external commonsense knowledge. We discover that aside from the knowledge transfer machine, current densepose estimation systems either from RCNN based methods or fully-convolutional frameworks can be improved in terms of the accuracy of human densepose estimation. Solid experiments on densepose estimation benchmarks demonstrate the superiority and generalizability of our approach.

Main Results on Densepose-COCO validation set

Environment

The code is developed based on the Detectron2 platform. NVIDIA GPUs are needed. The code is developed and tested using 4 NVIDIA RTX GPU cards. Other platforms or GPU cards are not fully tested.

Installation

Please follow the installation instruction to build environment.

Training and Testing

Training on COCO dataset using pretrained keypont models(Baidu. Extraction Code:fccy)

CUDA_VISIBLE_DEVICES=0,1,2,3 python projects/KTN/train_net.py \
--num-gpus 4 \
--config-file projects/KTN/configs/densepose_R_50_FPN_KTN_net_s1x.yaml \
OUTPUT_DIR coco_dp_exps/DensePose_ResNet50_KTN_Net_1lx

After training, the final model is saved in OUTPUT_DIR.

Testing on COCO dataset using provided models(Baidu. Extraction Code:fccy)