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VTuber software

This is a list of the most relevant vtubing software. The information is as accurate as I could figure it out, but there might be errors or some details might change. If you find anything that needs to be corrected, please let me know.

Additional explanations:

• iPhone means that an iPhone is basically required
• iFacialMocap support means that tracking data can be received from the iFacialMocap iPhone app
• VMC protocol means that the application can send and/or receive tracking data from other VMC protocol capable applications, allowing the combination of multiple tracking methods (e.g. VSeeFace receiving VR tracking from Virtual Motion Capture and iPhone/ARKit face tracking from Waidayo)
• Tobii means that the Tobii eye tracker is supported

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from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)

# if only categorical data has missing value
# will need to handle differently if there are missing numerical data
train_preprocessed = oe.fit_transform(train_df.fillna("NaN"))
test_preprocessed = oe.transform(test_df.fillna("NaN"))
train_preprocessed = pd.DataFrame(train_preprocessed, columns=train_df.columns)
test_preprocessed = pd.DataFrame(test_preprocessed, columns=train_df.columns)

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# Sometime we will have multiple input data in different dimensions
# such as wide & deep neural network suggestion model
import numpy as np
from imblearn.over_sampling import RandomOverSampler as ros

Xab = {"a":np.random.random_sample, "b": np.random.random_sample}
Y = [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
X_i = [[i] for i in range(len(Y))]
X_a = np.array([Xab['a'](1) for y in Y])
X_b = np.array([Xab['b'](2) for y in Y])