Comments (7)
That is correct, we use rest shape of the sheep/human only to train PoseNet.
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Hi, the details about PoseNet training are in the paper supplement B.1.
The training data are rendered on the fly. For more details, see here the training code of PoseNet, as well as the rendering + augmentation pipeline. Unfortunately, the sheep mesh is under commercial license and we are not allowed to release it.
occ
is the flow confidence computed by forward-backward check. It is renamed as cfd_at_samp
and used here to weight flow reconstruction loss.
from banmo.
Thanks for your reply!
I have a couple more follow-up questions
- Is there any way to get access to the sheep mesh and its surface features? Which SMPL mesh did you use?
- Whether human.pth and quad.pth were trained posenet using the script you mentioned here?
from banmo.
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The sheep/SMPL mesh/surface features are from densepose-CSE. You may get the vertex features but I'm afraid it's not easy to get their vertex locations if you are outside the organization (even I don't have access to them now). A relevant issue is opened here. The SMPL mesh is a subdivided version of the original one as noted here.
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Yes.
from banmo.
Hi @gengshan-y, after looking through the training code of PoseNet, I would like to ask that did you only use a single human mesh to train PoseNet for humans, and another sheep mesh to train PoseNet for quadruped animals ?
If we put aside that there are various types of humans and animals, that might be handled well by the pretrained CSE features. How can your PoseNet predict the root pose very well when the objects make various poses ?
from banmo.
Yes.
The initial poses passed into banmo are noisy indeed due to deformations/shape variations. BANMo is updating the root poses during optimization. See here.
from banmo.
Thank you for your quick response. But I would like to clarify a little bit that my question is about the function forward_warmup that you used to pre-train PoseNet, which is stored in human.pth and and quad.pth.
I was wondering if they were trained using a single mesh, e.g., the mesh of resting pose sheep (illustrated in Fig.12 of your paper), with random camera poses generated around it ?
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Related Issues (20)
- Questions about the synthetic datasets HOT 9
- Training BANMo using my own videos HOT 6
- SOS, the cat is so cute~~~~~ I cannot focused on codes! o(=•ェ•=)m
- bones_dfm or bones_rst when using gauss_mlp_skinning? HOT 2
- Canonical embeddings matching HOT 6
- Questions on the evaluation on root pose prediction HOT 2
- Question for Nerfies experiment HOT 6
- Question about mesh of Viser in Banmo HOT 2
- Generation of synthetic datasets HOT 3
- No "from pytorch3d import _C"
- Visualize of articulated shape HOT 1
- Optimized models on dropbox HOT 2
- update_delta_rts
- Bone reinitialization HOT 1
- Volume rendering HOT 2
- Question about result : a difference between the results in the paper and my results HOT 6
- Questions by using pre-optimized models HOT 7
- issue of "tmp/nvs-5-0-traj-all.mp4: No such file or directory" when trying pre-optimized models HOT 6
- some problem with colab demo HOT 1
- can banmo be used for 3D reconstruction of birds? HOT 4
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