Comments (3)
gsygsy96
commented on July 17, 2024
BTW, Smplify is a time-consuming optimization method. Could you share some details to efficiently fitting large benchmarks, such as COCO?
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mks0601
commented on July 17, 2024
SMPL Mosh fit is not accessible because of the license issue :( It's been maybe more than 1 year? so I couldn't calculate the error of Mosh fit. I reported error of my SMPLify-X fit to my paper (Table 13. 13.1mm). Also, for the time, I divide the dataset into several chunks and ran it on each GPU. Or you can estimate initial parameters using neural network and reduce the number of iteration following SPIN.
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gsygsy96
commented on July 17, 2024
Thanks for your help! I will try it!
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Related Issues (20)
- I have googled it, but it has not been solved yet. I have the following problem, please help me
- The provided freihand pose param is different from freihand origin dataset HOT 3
- help for size mismatch problem HOT 1
- Training Settings for FreiHAND HOT 6
- Question about downsampled mesh performance HOT 1
- MSCOCO Background HOT 3
- issues about mano param in freihand dataset HOT 1
- pytorch matrix size incorrect HOT 1
- bbox_root_freihand_output.json HOT 2
- training the rootnet with Freihand Dataset. HOT 3
- the focal of rootnet training HOT 1
- Change to MANO in demo.py HOT 1
- The thickness of the generated mesh HOT 2
- when running the command: python demo.py --gpu 3 --stage param --test_epoch 8 I get the following error, can someone help me please to solve it HOT 1
- clarification of OLD issue - Smpl body to image projection
- Question about joint numbers HOT 3
- KeyError: 'mesh_coord_cam' HOT 4
- Issue about h36m_smpl dataset HOT 11
- Issue about h36m dataset HOT 4
- freihand_train_data.json format HOT 2
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