MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling

MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling
Zhenyu Zhang^*, Wenhao Chai^*, Zhongyu Jiang, Tian Ye, Mingli Song, Jenq-Neng Hwang, Gaoang Wang
arXiv 2023.

Install

pip install torch matplotlib

Dataset preparation

Our model is evaluated on Human3.6M and MPI-INF-3DHP datasets, and we ese AMASS dataset for better pre-training.

Human3.6M and MPI-INF-3DHP

Dataset setting is same as this repo P-STMO. You can download the processed .npz file from their repo and put the .npz files in ./dataset folder.

AMASS

coming soon

Evaluating our models

Checkpoint: link

evaluate on Human3.6M (CPN)

python trainer.py -f 243 --n_joints 17 --gpu 0,1 --reload 1 --layers 4 -tds 2 --previous_dir x.pth --refine --refine_reload 1 --previous_refine_name x_refine.pth

evaluate on Human3.6M (GT)

python trainer.py -f 243 k gt  --n_joints 17 --gpu 0,1 --reload 1 --layers 4 -tds 2 --previous_dir x.pth --refine --refine_reload 1 --previous_refine_name x_refine.pth

evaluate on MPI_INF_3DHP(GT)

python trainer_3dhp.py -f 243 --n_joints 16 --gpu 0,1 --reload 1 --layers 4 -tds 1 --previous_dir x.pth --refine --refine_reload 1 --previous_refine_name x.pth

Pretraining from scratch

Prepare Poseaug Generator

You should follow the instructions in poseaug and got generator checkpoint for human3.6M. Then put the generator checkpoints in ./Augpart/chk foler. You can put as many as you can get and modify the list in file ./Augpart/gan_preparation.py

Pretrain a model for 17 joints (only on h36m dataset)

python pretrainer.py --MAE -f 243 --train 1 -k gt --n_joints 17 -b 1024 -tds 2 --layers 4 --dataset h36m --lr 0.0001 -lrd 0.97 -tmr 0.6 -smn 5 --gpu x,y --name task_name

Pretrain a model for 16 joints (only on h36m dataset with poseaug)

python pretrainer.py --MAE -f 243 --train 1 -k gt --n_joints 16 -b 1024 -tds 2 --dataset h36m --lr 0.0001 -lrd 0.97 --layers 4 -tmr 0.6 -smn 5 --gpu x,y --name task_name

Pretrain a model for 16 joints (on multiple dataset)

python pretrainer.py --MAE -f 243 --n_joints 16 -b 1024 -k gt -tds 2 --train 1 --dataset h36m,3dhp,amass --layers 3 --lr 0.0001 -lrd 0.97 -tmr 0.6 -smn 5 --gpu x,y --name task_name

Train HPE Task on h36m from scratch

N_JOINTS x and Layers n hould keep consistent with the pre-trained model.

python trainer.py -f 243 -k gt --train 1 --n_joints x -b 1024 --gpu 0,1 --lr 0.0007 -lrd 0.97  --layers 4 -tds 2 (--MAEreload 1 --previous_dir /path/to/pretrainedcheckpoint)(optional)

After training on human3.6M dataset, you can refine the model by:

python trainer.py -f 243 -k gt --train 1 --n_joints x -b 1024 --gpu 0,1 --lr 0.0001 -lrd 0.97  --layers 4 -tds 2 --reload 1 --previous_dir /path/to/bestcheckpoint --refine

Train 3DHPE on mpi_inf_3dhp from scratch

Finetune 3DHPE Model for MPI_INF_3DHP with 16 joints:

python trainer_3dhp.py -f 243 -k gt --train 1 --n_joints 16 -b 1024 --gpu 0,1 --lr 0.0007 -lrd 0.97 --layers 3 -tds 1 (--MAEreload 1 --previous_dir /path/to/pretrainedcheckpoint)(optional)

Train imcomplete 2D->3D Model

You can reload pretrained model or train model without reloading checkpoint:

python pretrainer.py -f 27 -b 2048 --model MAE -k gt --train 1 --layers 3 -tds 2 --lr 0.0002 -lrd 0.97 --name maskedliftcam -tmr 0 -smn 6 --gpu 0,1 --dataset h36m --MAE --comp2dlift 1 
(--MAEreload 1 --MAEcp /path/to/model)(optional)

Train imcomplete 3D -> 3D Model