AGORA

AGORA dataset parsing process

Cleaning SMPL parameters

• This process will convert CudaFloatTensor smpl parameters to numpy format.
• Download and unzip smpl_gt.zip and smplx_gt.zip from here.
• Run python tensor_to_numpy_parameter.py --dataset_path $PATH1. $PATH1 denotes AGORA dataset path.

Make annotation files

• This code will dump GT 2D/3D joints and 3D vertices of SMPL and SMPL-X in $PATH1. Also, it will generate AGORA_train.json and AGORA_validation.json in $PATH1.
• Download and unzip train_SMPL.zip, train_SMPLX.zip, validation_SMPL.zip, and validation_SMPLX.zip from here.
• Run python agora2coco.py --dataset_path $PATH1 --human_model_path $PATH2. $PATH1 denotes AGORA dataset path. $PATH2 denotes human model layer path.

Preparing 1280x720 image files

• This code will prepare 1280x720 image files.
• Download and unzip 1280x720 image files.
• Then, make 1280x720 folder in AGORA dataset path.
• For the $i$th zip file of training set, make train_$i$ folder and move all image files to that folder. For example, make train_0 folder at AGORA dataset path and move all image files from train_images_1280x720_0.zip to that folder.
• For the images of validation and test sets, make validation and test folders and move all images files to corresponding folders.

Preparing 3840x2160 image files

• This code will prepare 3840x2160 image files.
• Do the same process of 1280x720 image files
• As the image resolution is too high, you need to crop and resize humans to prevent the dataloader from being stuck.
• To this end, run python affine_transom.py --dataset_path $PATH1 --out_height 512 --out_width 384. $PATH1 denotes AGORA dataset path.

Download AGORA_test_bbox.json

• Download human detection results on test set from here
• The human detection results are from YOLO v5.

Final directory

${PATH1}
|-- AGORA_train.json
|-- AGORA_validation.json
|-- AGORA_test_bbox.json
|-- gt_joints_2d
|-- |-- smpl
|-- |-- smplx
|-- gt_joints_3d
|-- |-- smpl
|-- |-- smplx
|-- gt_verts
|-- |-- smpl
|-- |-- smplx
|-- 1280x720
|   |-- train_0
|   |-- train_1
|   |-- train_2
|   |-- train_3
|   |-- train_4
|   |-- train_5
|   |-- train_6
|   |-- train_7
|   |-- train_8
|   |-- train_9
|   |-- validation
|   |-- test
|-- 3840x2160
|   |-- train_0
|   |-- train_0_crop
|   |-- train_1
|   |-- train_1_crop
|   |-- train_2
|   |-- train_2_crop
|   |-- train_3
|   |-- train_3_crop
|   |-- train_4
|   |-- train_4_crop
|   |-- train_5
|   |-- train_5_crop
|   |-- train_6
|   |-- train_6_crop
|   |-- train_7
|   |-- train_7_crop
|   |-- train_8
|   |-- train_8_crop
|   |-- train_9
|   |-- train_9_crop
|   |-- validation
|   |-- validation_crop
|   |-- test
|   |-- test_crop