Thank you for sharing. I tried to reproduce your experimental results, but both results were far from those given in your paper.

First result: MPJPE from mesh: 493.55 mm PA MPJPE from mesh: 112.53 mm MPVPE from mesh: 529.30 mm

Second result: MPJPE from mesh: 644.32 mm PA MPJPE from mesh: 103.72 mm MPVPE from mesh: 661.93 mm

Can you share the hyperparameter setting or give some instructions? pytorch:1.6,cuda:10.2

Hi, Thank you for your fantastic work! However, I tried to reproduce table8. using the command python train.py --amp --continue --gpu 0 --cfg ../assets/yaml/3dpw.yml and got the result below:

* train for 10 epoch
	MPJPE from mesh: 93.76 mm
	PA MPJPE from mesh: 56.54 mm
	MPVPE from mesh: 110.48 mm
* train for 5 epoch
	MPJPE from mesh: 85.21 mm
	PA MPJPE from mesh: 52.53 mm
	MPVPE from mesh: 101.27 mm
* train for 4 epoch
        MPJPE from mesh: 85.65 mm
        PA MPJPE from mesh: 51.91 mm
        MPVPE from mesh: 101.10 mm
* train for 2 epoch
	MPJPE from mesh: 84.16 mm
	PA MPJPE from mesh: 52.13 mm
	MPVPE from mesh: 99.90 mm
* train for 1 epoch
	MPJPE from mesh: 83.17 mm
	PA MPJPE from mesh: 52.34 mm
	MPVPE from mesh: 99.32 mm

* train for 0 epoch(pretrained)
	MPJPE from mesh: 712.35 mm
	PA MPJPE from mesh: 98.89 mm
	MPVPE from mesh: 734.46 mm

Is it normal to get to the best point only after 1 epoch? (BTW, I used the pre-trained ResNet-50 weights of xiao2018simple) Also is it normal for the results to be worse as the epoch increasing?

Thank you!

Thank you for your work!

Can you please provide the code and json file(2D Pose Result) to evalution on the CMU-Panoptic dataset?

================================================================================= 안녕하세요! Github에 논문 코드를 올려주셔서 감사합니다!

Code를 확인해본 결과 CMU-Panoptic 결과를 내면서 사용한 python file과 2D Pose estimation json 파일이 없는 것 같아서 요청드리고자 이슈에 올렸습니다!

감사합니다

Your paper reports evaluation resluts of MuPoTS-3D, but there are several procedures I could not fully understand. 1) Your data loading script(data/MuPoTs/MuPoTs.py) reads preprocessed 2d pose estimator results based on annotation id. Does this mean that you used ground-truth bounding boxes for retrieving 2d predictions? 2) At https://github.com/hongsukchoi/3DCrowdNet_RELEASE/blob/c4d32c0dbb2675b022f9bc2848a39242d1142244/data/MuPoTs/MuPoTs.py#L80, your code specifies to use joint regressor weights of mpi-inf-3dhp dataset. Is this file made by yourself? As far as I know, previous works(including https://github.com/JiangWenPL/multiperson) used J_regressor_extra.npy for evaluation on mpi-inf-3dhp and MuPoTS.

Hi, thanks for sharing your code. I'm wondering how to reproduce the result in table6 and table7. Could your offer your preprocessed json file, config file and dataset file?

Hi, thanks for your excellent work! But when I tried to reproduce the results, the 3DPW parsed data link listed in https://github.com/hongsukchoi/3DCrowdNet_RELEASE/blob/3d84d17797e353aaf49acfd8087637eca2877292/assets/directory.md is broken. It requires access, but I couldn't send a request.

Could you fix this problem?

Thanks for the great work. I am trying to run 3DPW evaluation script, but the test script raises an error due to nonexistence of J_regressor_coco_hip_smpl.npy file. It seems that you did not provide a download link for it. Is this file the same as this one? https://github.com/mks0601/I2L-MeshNet_RELEASE/blob/master/data/MSCOCO/J_regressor_coco_hip_smpl.npy

Hi! Firstly, thanks for your excellent work! I was learning your data processing procedure and had confusions about the following code: https://github.com/hongsukchoi/3DCrowdNet_RELEASE/blob/fde86df0fb8799873cd19c683b8251b16640acb3/data/Human36M/Human36M.py#L216 Could you kindly explain to me that what the physical meaning of 'smpl_trans - root_joint_coord + np.dot(R, root_joint_coord.transpose(1,0)).transpose(1,0)' is? As far as i understand that 'smpl_trans - root_joint_coord' represents the translation in the coordinate system that takes root joint as origin. My question is how to explain np.dot(R, root_joint_coord.transpose(1,0)).transpose(1,0)?

I am trying to run train script, but there is an issue about annotation file. Current code tries to read 'MSCOCO_train_SMPL_NeuralAnnot.json' insead of 'coco_smplifyx_train.json': https://github.com/hongsukchoi/3DCrowdNet_RELEASE/blob/c4d32c0dbb2675b022f9bc2848a39242d1142244/data/MSCOCO/MSCOCO.py#L66

Is it correct to comment the 66th line and uncomment the 67th line? Or should we use 'MSCOCO_train_SMPL_NeuralAnnot.json'?

@hongsukchoi

Hello! 3DCrowdNet is a nice work!

I have a few questions about Table 1 and network.

• Is Table 1 result tested on the 3DPW-Crowd?
• Is Table 1 result trained on a mixed dataset or a single dataset?
• I don't quite understand this operation. The img_feat is a 2d image-space feature. For sampling on it, the 3d joint may use a perspective projection to get the 2d image space point. Why just use the x,y of the 3d joint?
• When you are testing your idea of crowded-scene robust, do you test on the 3DPW-crowd instead of the large whole 3dpw test set? If the results are good on 3dpw-crowd, then you test on the whole 3dpw test set. Do the research procedure I describe correct?

Sorry to bother you again. :( I have a question about the SMPL parameters in the 3dpw dataset you offered. I noticed the pose parameter (72,) and trans parameters (3,) differ from the official 3dpw dataset. So, I guess there are some preprocess in making the annotation. Could you tell me how you preprocess it? Thanks in advance.~

Hi. thanks for sharing the nice work.

In your code, You use SMPLify-X pseudo GT on 3D datasets(H36M, MuCo), and use Neural Annot pseudo GT on 2D datasets(CrowdPose, MSCOCO). Can I ask why you don't Neural Annot on H36M, MuCo?

Hello!

Trying to reproduce the provided demo I get the following error: File "demo.py", line 114, in model.load_state_dict(ckpt['network'], strict=False) File "<path_to_condaenv>/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1668, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for DataParallel: size mismatch for module.human_model_layer.th_betas: copying a param with shape torch.Size([1, 10]) from checkpoint, the shape in current model is torch.Size([1, 300]). size mismatch for module.human_model_layer.th_shapedirs: copying a param with shape torch.Size([6890, 3, 10]) from checkpoint, the shape in current model is torch.Size([6890, 3, 300]).

It seems pytorch is failing to load the pretrained 3DCrowdNet model referred to in the README. Does someone have a hint on how to fix this and why this might happen?

My setup: Debian 11 (bullseye) Anaconda version: 22.9.0 Python version in Anaconda environment: 3.7.3 PyTorch version: 1.13.0+cu117

Thank you very much!

https://github.com/hongsukchoi/3DCrowdNet_RELEASE/blob/fde86df0fb8799873cd19c683b8251b16640acb3/main/model.py#L38

Hello @hongsukchoi First, thanks for the nice work you've publicly released! Currently I'm doing a research on the multi-person reconstruction and while reading your code line by line I encountered the function get_camera_trans which I mentioned above.

As far as I understand, from RotationNet we get cam_param and the function converts it into cam_trans (which stands for camera translation matrix) and again it goes in to get_coord function.

My QUESTION starts here! I don't understand the exact meaning or assumption of gamma and k_value. Is there any theory referring to it?

You seems like replying to issues so.. Thanks in advance!

Joseph

Hi, I tried to reproduce table 8 without pre-trained ResNet-50 weights of xiao2018simple. My training command is python train.py --amp --gpu 0 --cfg ../assets/yaml/3dpw_crowd.yml and the config file is :

trainset_3d: ['Human36M', 'MuCo']
trainset_2d: ['MSCOCO', 'MPII']
testset: 'PW3D'

lr_dec_epoch: [30]
end_epoch: 40
lr: 0.00025 #0.001/4
lr_backbone: 0.0001
lr_dec_factor: 10

However, I got very strange results on 3dpw as below (I evaluate every epoch):

Do you have any idea about this? Thank you!