Comments (2)
zhan-xu
commented on August 13, 2024
Hi. The volumetric geodesic calculation is the most time consuming step. I used a cluster of nodes to accelerate. With a single machine, you may consider down-sampling. Take a look at quick_start.py, where I have a down sampling option. You may first consider 1K or even 512 vertices, and then use nearest neighbor to upsample distance map to full resolution.
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countywest
commented on August 13, 2024
Thanks a lot! It seems processing is much boosted with the approximation.
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Related Issues (20)
- Is it possible to process asymmetrical model? HOT 5
- 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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