Comments (5)
Hi! Our training data has some asymmetric models, although the number is not high.
The simplest way to remove symmetry constraint is by commenting out some code. Suppose you are using quick_start.py, you can:
(1) remove line 130-133 where I reflect shifted pts and attention.
(2) remove line 151 where I reflect predicted joints
(3) line 204, use primMST
instead of the heuristic primMST_symmetry
. (imported from utils.mst_utils.py, and ignore the "joints" parameter)
I hope this can work... but I assume you also need to tune the hyper-parameters bandwidth and threshold.
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Hi, thanks for your nice work!
I'v tested some asymmetrical obj data with your pretrained models, found that the results were not very good, as shown in the following figures:
Could you give me some suggestions to optimize the results? Is it possible to fix it by adding some asymmetrical data in the training set?
Thank you in advance!
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Cool, it works. Thanks for your help !!
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Hi, I'd like to preprocess your dataset from scratch, and met some problems in obtaining the rig_info_remesh.
I have got the remeshed obj files within 1K and 5K vertices, but don't know how to recalculate the skinning....
Could you provide some example code or more details about it ? Thank you!
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Hi. For vertex on the remeshed mesh, I just copy the skinning weights from its nearest vertex on the original mesh.
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Related Issues (20)
- OSError problem HOT 2
- run_joint_pretrain issue on macos
- How to reduce/eliminate the "randomness" of the predicted skeleton? HOT 4
- Is it possible to run without any cuda because I haven't cuda in my machine HOT 1
- Is it possible to generating fixed joints with certain topology? HOT 3
- 可以提供colab版吗?
- Running RigNet in python3.9 and get Aborted HOT 11
- the issue on Dataset Directory variable (DATASET_DIR) for training
- Imcomplete skeleton
- The link of the dataset has been removed. HOT 3
- Data licensing HOT 1
- Code to compute metrics is missing HOT 5
- Can we do rig on custom SMPL ?
- Bad skinning/weights issue HOT 3
- Running `quick_start.py` Error HOT 1
- Compared to NeuroSkinning, regarding the skin of clothing parts
- std::bad_alloc Error
- Why normalize? HOT 1
- trained_models not working HOT 1
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