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Real-World Evaluation Images for Articulated Objects Interaction Generation
This dataset contains the real-world images used in evaluating DragAPart, a conditional image generator that models interaction with articulated objects.
๐ฆ How to Use It?
Each sample consists of:
original_image_XXX.png
: The base image showing an articulated object.arrow_locations_XXX.npy
: A NumPy file containing the arrow coordinates for interaction.
The .npy
file stores one arrow as:
[x0, y0, x1, y1] # Normalized coordinates in [0, 1]
Where:
(x0, y0)
is the starting point of the interaction (e.g., where the user clicks),(x1, y1)
is the end point indicating the direction or extent of the manipulation.
These coordinates are normalized relative to the image size.
๐ผ๏ธ Visualization
You can visualize the interaction using the following Python script:
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
# Load image and arrow data
image_path = "original_image_000.png"
arrow_path = "arrow_locations_000.npy"
image = Image.open(image_path)
arrow = np.load(arrow_path)[0] # [x0, y0, x1, y1]
# Convert normalized coordinates to pixel values
width, height = image.size
x0, y0 = int(arrow[0] * width), int(arrow[1] * height)
x1, y1 = int(arrow[2] * width), int(arrow[3] * height)
# Plot the image and overlay the interaction arrow
plt.figure(figsize=(6, 6))
plt.imshow(image)
plt.arrow(x0, y0, x1 - x0, y1 - y0,
color='red', width=2, head_width=10, length_includes_head=True)
plt.axis('off')
plt.title("Interactive Manipulation Arrow")
plt.show()
This will display the original image with a red arrow showing the suggested user interaction as below:
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