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- .gitattributes +2 -0
- data/data_examples/README.md +57 -0
- data/data_examples/Tshirt_folding_hands_rgb/000045.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000050.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000055.jpg +3 -0
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- data/data_examples/Tshirt_folding_hands_rgb/000065.jpg +3 -0
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- data/data_examples/Tshirt_folding_hands_rgb/000110.jpg +3 -0
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- data/data_examples/Tshirt_folding_hands_rgb/000150.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000155.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000160.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000165.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000170.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000175.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000180.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000185.jpg +3 -0
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- data/data_examples/Tshirt_folding_hands_rgb/000195.jpg +3 -0
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- data/data_examples/Tshirt_folding_hands_rgb/000210.jpg +3 -0
- data/data_examples/Tshirt_folding_hands_rgb/000215.jpg +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00068/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00068/volume/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00068/volume/nocs_winding_number_field/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00068/volume/nocs_winding_number_field/128/.zarray +24 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00068/volume/nocs_winding_number_field/128/0.0.0 +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00074/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00074/volume/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00074/volume/nocs_winding_number_field/.zgroup +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00074/volume/nocs_winding_number_field/128/.zarray +24 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/instances/00074/volume/nocs_winding_number_field/128/0.0.0 +3 -0
- data/data_examples/VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt/samples/.zgroup +3 -0
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@@ -1026,3 +1026,5 @@ data/flattening/flattening_dataset.z96 filter=lfs diff=lfs merge=lfs -text
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# Samples of VR-Folding dataset
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## Data structure
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We provide 1 video sequence for *Folding* task.
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### RGB images
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The following diretory contains RGB images of the video sequence rendered with Unity. Note that these images are only for visualization, so we have rendered both hands additionally.
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- `Tshirt_folding_hands_rgb`
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### Processed Data: Zarr data
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All the multi-view RGB-D images will be transformed into point clouds and merged together into [zarr](https://zarr.readthedocs.io/en/stable/) format. All the other annotations are also contained in the data with [zarr](https://zarr.readthedocs.io/en/stable/) format. The following diretory contain samples for *Folding* task in [zarr](https://zarr.readthedocs.io/en/stable/) format.
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- `VR_Folding/vr_simulation_folding_dataset_example.zarr/Tshirt`
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Here is the detailed tree structure of a data example of one frame.
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```
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00068_Tshirt_000000_000000
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├── grip_vertex_id
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│ ├── left_grip_vertex_id (1,) int32
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│ └── right_grip_vertex_id (1,) int32
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├── hand_pose
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│ ├── left_hand_euler (25, 3) float32
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│ ├── left_hand_pos (25, 3) float32
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│ ├── right_hand_euler (25, 3) float32
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│ └── right_hand_pos (25, 3) float32
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├── marching_cube_mesh
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│ ├── is_vertex_on_surface (6410,) bool
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│ ├── marching_cube_faces (12816, 3) int32
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│ └── marching_cube_verts (6410, 3) float32
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├── mesh
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│ ├── cloth_faces_tri (8312, 3) int32
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│ ├── cloth_nocs_verts (4434, 3) float32
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│ └── cloth_verts (4434, 3) float32
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└── point_cloud
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├── cls (30000,) uint8
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├── nocs (30000, 3) float16
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├── point (30000, 3) float16
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├── rgb (30000, 3) uint8
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└── sizes (4,) int64
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```
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## Visualization
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We provide a simple script for visualizing data in [zarr](https://zarr.readthedocs.io/en/stable/) format. This script will filter out the static frames (i.e. garment pose remains unchanged) in the video and only visualize dynamic frames.
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### Setup
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Requirements: Python >= 3.8
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This code has been tested on Windows 10 and Ubuntu 18.04.
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```
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pip install -r requirements.txt
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```
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### Run
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```
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python vis_samples.py
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```
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This script will use Open3D to visualize the following elements:
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- the input partial point cloud with colors
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- the grasping points of both hands (represented by blue and red spheres)
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- the complete GT mesh colored with NOCS coordinates
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Note that our recorded data in Zarr format contains complete hand poses (positions and euler angles of 25 bones for each hand).
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In this simplified 3D visualization script, we only visualize the valid grasping points on the garment surface instead of complete hands for fast implementation.
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