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Overview

This repository is a PyTorch implementation of the paper Prior-guided Source-free Domain Adaptation for Human Pose Estimation published at ICCV 2023.

Our code is based on the implementation of UDA_PoseEstimation and RegDA.

Data Preparation

As instructed by UDA_PoseEstimation, following datasets can be downloaded automatically:

• Surreal Dataset
• LSP Dataset

You need to prepare the following datasets manually if you want to use them:

• Human3.6M Dataset

Train human pose prior

• See prior folder for instructions

Source-free UDA

• SURREAL -> LSP

python train_human_prior.py /data/AmitRoyChowdhury/dripta/surreal_processed /data/AmitRoyChowdhury/dripta/lsp -s SURREAL -t LSP --target-train LSP_mt --log logs/s2l/ --seed 0  --lambda_c 1 --epochs 70 --pretrain-epoch 40 --rotation_stu 60 --shear_stu -30 30 --translate_stu 0.05 0.05 --scale_stu 0.6 1.3 --color_stu 0.25 --blur_stu 0 --rotation_tea 60 --shear_tea -30 30 --translate_tea 0.05 0.05 --scale_tea 0.6 1.3 --color_tea 0.25 --blur_tea 0 -b 32 --mask-ratio 0.5 --k 1 --occlude-rate 0.5 --occlude-thresh 0.9 --prior prior/SURREAL/K_5/checkpoints/l2/prior_stage_3.pt --fix-head --fix-upsample --source-free --lambda_b 1e-3 --lambda_p 1e-6 --step_p 47