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moberweger avatar moberweger commented on July 21, 2024

from deep-prior-pp.

laserwave avatar laserwave commented on July 21, 2024

Thank you. I use the pretrained parameters of MSRA15 dataset you provide to run the test_realtimepipeline.py.
Here is the settings and results of 'P0' sequence. But the result is not good. Is that right?

poseNetParams = PoseRegNetParams(type=11, nChan=1, wIn=128, hIn=128, batchSize=128, numJoints=21, nDims=3)
poseNetParams.loadFile = "MSRA_network_prior_0.pkl"
comrefNetParams = ScaleNetParams(type=1, nChan=1, wIn=128, hIn=128, batchSize=1, resizeFactor=2, numJoints=1, nDims=3)
comrefNetParams.loadFile = "net_MSRA15_COM_AUGMENT.pkl"
config = {'fx': 241.42, 'fy': 241.42, 'cube': (250, 250, 250)}
screenshot-20221018-211805
screenshot-20221018-211818

from deep-prior-pp.

moberweger avatar moberweger commented on July 21, 2024

Interesting effect, thanks for sharing. It certainly does not look correct, it should work much better. Did you check 1) the networks are loaded correctly, 2) the data is in the correct format, 3) the crop for the networks is in the correct format, 4) it seems you run with "com" mode, you could try the tracking mode with refined location enabled.

from deep-prior-pp.

laserwave avatar laserwave commented on July 21, 2024

The result is much better from the third frame of the sequence using the tracking mode. Why the first two seem still bad and the following correct?

image
(the third frame)

from deep-prior-pp.

moberweger avatar moberweger commented on July 21, 2024

Yes, that one looks much better. Possibly something is wrongly initialized? I am not sure about the details how you run it.

from deep-prior-pp.

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