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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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+
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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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+
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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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+
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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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+
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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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