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Figure 11 An example frame of bad estimation of a weak pose in IXMAS dataset.
Figure 9 An example of bad estimation of a weak pose in HumanEva dataset.
To visualize results of weak pose reconstruction, we project weak poses from UaSpace back to the original parameter space.
where ψ j i ′ is the weak pose corresponding to the j-th image frame in i-th video sequence.
For each dimension of the weak pose parameter space, we define a Gaussian process to map from 2D image features to this particular dimension.
"Std-norm" and "Ind-norm" refer to standard deviation normalizing method and individually normalizing method (refer to Section Weak pose estimation using GPR).
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To verify the effect of the incorporation of weak poses.
Then, we select k most representatives weak poses F k from Fpre with k-means.
Figure 8 Two exampled frames of good estimation of weak poses in HumanEva dataset.
We have applied GPR to model the mapping from silhouettes to weak poses.
With the predicted weak poses, the problem turns into a classification problem in the UaSpace.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com