What is the experimental setup for human mesh recovery? about smoothnet HOT 7 OPEN

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.

from smoothnet.

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.

from smoothnet.

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.

from smoothnet.