Comments (7)
In addition, I noticed that the data from data/poses/pw3d_vibe_smpl contains 37 sequences. But this does not match with the 3DPW dataset, which has 24 sequences in test_set, 12 sequences in vald_set and 24 sequences in train_set.
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Hi @HospitableHost ,
We used the 3dpw-spin for training. The differences may come from that we removed a few frames at the end of each period that cannot be divided by sliding windows. Did you use our provided code and data for the test?
For 37 sequences, there are some sequences that have two persons. We simply split them into two sequences.
from smoothnet.
@ailingzengzzz Hi, I read your code carefully and tested with your code and data. (data/poses/pw3d_vibe_smpl/...) Therefore, I don't know what caused the VIBE results to be different from your paper.
By the way, I still have three questions:
first, did you use spin---3dpw_train_set for training, right?
second, do the 37 sequences correspond to the 3dpw_test_set?
third, could you please open-source the pretrained model: 3DPW-SPIN-SMPL (not 3DPW-SPIN-3D)
best wishes!
from smoothnet.
Hi @HospitableHost,
The answer to questions 1 and 2 is yes.
We explored the 3DPW-SPIN-SMPL for SMPL (6d rotation matrix) testing but found it is inferior to 3DPW-SPIN-3D (see the paper). Thus, we only provided the 3d model for cross-modality testing.
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Hi @ailingzengzzz
So have you figured out why the results of VIBE are inconsistent with the paper?
I suspect that there is something wrong with the data in the folder data/poses/pw3d_vibe_smpl.
from smoothnet.
Besides, I run the evalution of this command :
CUDA_VISIBLE_DEVICES=7 python eval_smoothnet.py --cfg configs/pw3d_spin_3D.yaml --checkpoint data/checkpoints/pw3d_spin_3D/checkpoint_32.pth.tar --dataset_name pw3d --estimator spin --body_representation smpl --slide_window_size 32
The result is also different from your paper.
from smoothnet.
Hi @HospitableHost ,
The results in Table 3 are calculated via 3D keypoint positions following previous works (e.g., VIBE, PARE, HMR etc). They use a model to estimate SMPL parameters and transform them into 3D keypoint positions for MPJPE, PA-MPJPE, and ACCEL. We simply input the transformed 3D keypoint into SmoothNet to obtain the final 3D keypoint positions.
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Related Issues (20)
- Can smoothNet work with hand motion capture (such as mediapipe handlandmarker)? HOT 1
- VIBE Result is different from vibe paper HOT 1
- trian on h36m
- Motion-aware smoothnet HOT 1
- checkpoints is not a pretrained model!!!! HOT 1
- 3D pose estimator results are different from paper HOT 2
- RLE 2D,3D and other dateset HOT 1
- 该网络是否能够即插即用,兼容不同模型与数据集 HOT 1
- how to deal the differernt shape between vibe predicted and gt? HOT 1
- Replicate the Motion-aware SmoothNet HOT 1
- Can i use SmoothNet to inference in realtime video? HOT 4
- SmoothNet SMPL model input HOT 1
- SMPL parameters as input HOT 1
- On the issue of temporal processing of 3DPW datasets HOT 1
- The results of the 3dpw data set are different from your paper HOT 2
- Whether the sliding window is only set during testing?
- The SmoothNet doesn't seem to be working
- How to use?
- Fail to Set UP Environment
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