2d and 3d joints positions about 4d-humans HOT 2 OPEN

You can follow the code in the forward_step to see how we calculate the 3D joints and project them to the image.
In what way were the 3D and 2D joints different from the ones you calculated?

from 4d-humans.

Thank for your reply. This is the code i use to visualize the first 25 predicted 2d joints on the original frame.
`import joblib
import cv2
import matplotlib.pyplot as plt
import numpy as np

data = joblib.load('/home/markusc/Videos/panda_test/video/output2/webvid/medium/3/4dhumans/3_4dhumans.pkl')

first_key = next(iter(data.keys()))
joints_2d_1 = data[first_key]['2d_joints'][0][:252]
joints_2d_2 = data[first_key]['2d_joints'][1][:252]

image_path = '/home/markusc/Videos/panda_test/video/3/json_files/multiple/3/0_pose.png'
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

img_height, img_width, _ = image.shape

joints_2d_1 = joints_2d_1.reshape(-1, 2)
joints_2d_2 = joints_2d_2.reshape(-1, 2)

joints_2d_1[:, 0] *= img_width
joints_2d_1[:, 1] *= img_height
joints_2d_2[:, 0] *= img_width
joints_2d_2[:, 1] *= img_height

print("Joints 2D 1 positions:")
print(joints_2d_1)

print("Joints 2D 2 positions:")
print(joints_2d_2)

plt.figure(figsize=(10, 10))
plt.imshow(image)

for idx, joint in enumerate(joints_2d_1):
if idx == 0:
plt.scatter(joint[0], joint[1], s=100, c='yellow', marker='o')
else:
plt.scatter(joint[0], joint[1], s=30, c='red', marker='o')
plt.text(joint[0], joint[1], str(idx), fontsize=12, color='red')

for idx, joint in enumerate(joints_2d_2):
if idx == 0:
plt.scatter(joint[0], joint[1], s=100, c='green', marker='o')
else:
plt.scatter(joint[0], joint[1], s=30, c='blue', marker='o')
plt.text(joint[0], joint[1], str(idx), fontsize=12, color='blue')

plt.title('2D Joints on Image - First Frame')
plt.axis('off')
plt.show()`

And the result is as follow

from 4d-humans.