Comments (7)
Hi, could you try this file? (need to save it as opts.log in the same directory as pre-trained models). I realized the one provided in the pre-trained model link is for quadruped animals.
This runs without problem for me.
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Many thanks for your reply! I try the opts-human.log you provided, but the result is the same as the above.
Ah, I know what causes the difference, it's due to missing the 'init-cam' folder. If 'init-cam' is absence, the inference results are like above.
So could you please provide the 'init-cam' files matching the pre-trained models?
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It should work without the init-cam folder. That not being used at inference time.
Was there a warning when executing the code with the following message?
!!!deleting video specific dicts due to size mismatch!!!
It would be best if you attach the a log from the console. Here's mine:
log.txt
Were you able to run the cat example without a problem?
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Many thanks for your reply! Yes, there is a warning as you mentioned. "!!!deleting video specific dicts due to size mismatch!!!" And I got the same warning and the same strange results when running the cat example. I attach my log here: log.txt
I check the warning code here. For my situation, the states['near_far'].shape[0] is 583, while the self.model.near_far.shape[0] is 582.
I don't know what causes this mismatch. Could you please tell me the potential reasons?
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This suggests you are missing an image in database/DAVIS/JPEGImages/Full-Resolution/human-cap006/
, and below is my folder (adult-500 is the old name of human-cap006)
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Oh! It works now if I replace human-cap006 datasets with the corresponding datasets you provided.
For the absent image issue, I retried preprocessed command below, but still, it extracts 00000-00037.jpg from human-cap_6.MOV, with 00038.jpg missing. (using ffmeng 3.4.8, pytorch 1.7.1, cuda 11.0)
bash preprocess/preprocess.sh human-cap_6 .MOV y 10
I am confused about what causes this difference. Could you please retry the command above to see if 00038.jpg could be extracted normally?
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Can confirm it works for me.
In the preprocessing script, we first use ffmpeg to extract frames, and then the frames are moved to tmp/$seqname/images.
Then preprocess/mask.py reads images and runs segmentation. The frames with good detections are saved to database/DAVIS/JPEGImages/...
Attaching the log of running mask.py below:
log.txt
Can you confirm ffmpeg or mask.py are producing expected outputs?
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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
- 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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