Inquiry About "4d_humans" Folder in NeuMan Dataset #6

TCN1214 · 2024-05-15T02:32:37Z

Hi, thanks for releasing the code. When I downloaded your NeuMan dataset, I noticed an additional folder called "4d_humans," which isn't present in the original NeuMan dataset. Could you let me know where you obtained the files in the "4d_humans" folder, such as smpl_optimized_aligned_scale.npz, smpl_aligned_scale.npz, poses_optimized.npz, and poses.npz?

chuanHN · 2024-05-31T09:17:51Z

Have you made any progress？ I have the same question here. @TCN1214

chuanHN · 2024-05-31T09:21:10Z

Anyone who is interested in motion capture can Joint wechat group through me. my wechat id is Denny0805789

ZCWzy · 2024-06-15T06:17:22Z

8.9 Edit
My code. I wrote my own data preprocessing module and it seems to work. I seem to have succeeded in using 4D-humans to completely replace ROMP , but there is still a little problem.
It can give you some reference.

7.9 Edit
So I found a workable way to generate smpl_optimised_aligned_scale.npz
u need coding by urself to process
But my training didn't turn out so well.

Using neuman's preprocessing process, you should get files other than smpl_optimised_aligned_scale.npz (sensepose,depthmaps,images,keypoints,monodepth,segments,smpl_pred ,sparse, etc.).
对于中国玩家，这个docker特别难搞，请多参考Neuman的issue，做好心理准备

In the preprocessing flow, change the function for solve_transformation in export_alignments.py to:

def solve_transformation(verts, j3d, j2d, plane_model, colmap_cap, smpl_cap):
    np.set_printoptions(suppress=True)
    mvp = np.matmul(smpl_cap.intrinsic_matrix, smpl_cap.extrinsic_matrix)
    trans = solve_translation(j3d, j2d, mvp)
    smpl_cap.cam_pose.camera_center_in_world -= trans[0]
    joints_world = (ray_utils.to_homogeneous(
        j3d) @ smpl_cap.cam_pose.world_to_camera.T @ colmap_cap.cam_pose.camera_to_world.T)[:, :3]
    scale = solve_scale(joints_world, colmap_cap, plane_model)

    transf = smpl_cap.cam_pose.world_to_camera.T * scale
    transf[3, 3] = 1
    transf = transf @ colmap_cap.cam_pose.camera_to_world_3x4.T

    rotation = smpl_cap.cam_pose.world_to_camera.T
    rotation[3, 3] = 1
    rotation = rotation @ colmap_cap.cam_pose.camera_to_world.T
    rotation = rotation[:3,:3]

    verts_world = ray_utils.to_homogeneous(verts) @ transf
    return transf, verts_world,rotation

Also remember to save the results of scale
scale = solve_scale(joints_world, colmap_cap, plane_model)

Human pose prediction using PHALP as mentioned by 4D-humans.
You can use the colab provided by 4D-humans.
!python track.py video.source=(your images path)
Note that the input is a jpg images, obtained from the folder images obtained in the previous step (need png->jpg)
Referring to the PHALP repository, this step will get xxx.pkl
With pkl, you get bbox, body_pose, betas, global_orient (all need to be processed)

Take the last line of alignments.npy as transl
Take the saved scale from above as scale
The bbox needs simple processing
body_pose needs to be converted from rotation matrix to axis angle
Regarding betas, I simply took the average.
For global_orient, refer to the rotation saved above:（Here the ROMP's predictions are actually used to align）

pose = []
file = joblib.load("smpl_output_romp.pkl")
file = file[1]['pose']
for it in file:
    go = [it[0], it[1], it[2]]
    pose.append(go)

global_orient = []
for go, rotation in zip(pose, rotations):
    go = angle_axis_to_quaternion(torch.tensor(go))  # 轴角转四元数
    go = quaternion_to_rotation_matrix(go)  # 四元数转旋转矩阵
    go = np.array(go)

    tmp = rotation.T @ go
    tmp = torch.tensor(tmp)
    tmp = rotation_matrix_to_angle_axis(tmp)
    tmp = np.array(tmp)

    global_orient.append(tmp)

then u will get smpl_optimised_aligned_scale.npz

yashgarg98 · 2024-06-29T03:59:20Z

@ZCWzy were you able to resolve this issue and train humans on your own data?

ZCWzy · 2024-06-29T09:20:02Z

您是否能够解决此问题并使用自己的数据来训练人类？

@yashgarg98 no, There is still a problem with the global_orient parameter.

TCN1214 · 2024-07-01T04:22:35Z

Hi, @ZCWzy, you mentioned that the body_pose in smpl_optimized_aligned_scale.npz comes from HMR_results (4D-humans), right? But when I print them out, they appear in different formats. For example, for per frame,

The body_pose in 4D humans:
Rotation Matrix(23x3x3),
[[[[ 9.9234e-01, 8.2207e-02, -9.2175e-02],
[-7.3918e-02, 9.9319e-01, 9.0004e-02],
[ 9.8947e-02, -8.2502e-02, 9.9167e-01]],
...........
[[ 9.9955e-01, 9.1900e-03, 2.8637e-02],
[-1.2969e-02, 9.9080e-01, 1.3470e-01],
[-2.7136e-02, -1.3501e-01, 9.9047e-01]]]]

The body_pose in smpl_optimized_aligned_scale.npz :
[[-1.47791445e-01 4.16232273e-02 5.25643043e-02 -1.22563608e-01
2.61688922e-02 6.45624648e-04 2.20337659e-01 2.87827626e-02
2.13257540e-02 8.55209112e-01 6.79941997e-02 -4.13512923e-02
8.58351827e-01 1.72289640e-01 5.71232103e-02 6.53865710e-02
-3.40895471e-03 1.18748546e-02 -4.41254586e-01 3.55301857e-01
7.91222006e-02 -3.93874198e-01 -1.79850206e-01 6.99728727e-02
8.66328552e-02 1.39882900e-02 -1.46647738e-02 -1.45833284e-01
1.92387864e-01 2.00581640e-01 -1.08115859e-01 1.29345153e-02
-1.83795810e-01 5.12605272e-02 5.69591857e-03 1.39512479e-01
3.74578275e-02 -4.10997570e-02 -2.82684803e-01 -1.65722191e-01
2.81276673e-01 7.73936436e-02 2.02245563e-01 -2.85247006e-02
-1.13176286e-01 2.31526673e-01 -9.19183530e-03 -1.00814617e+00
-9.39681306e-02 6.85916603e-01 5.57925045e-01 3.69742483e-01
-6.59028530e-01 -3.24325003e-02 -1.59564856e-02 1.36007953e+00
-4.22677845e-01 -1.38628498e-01 -2.11106837e-02 4.08339612e-02
5.21880051e-04 8.00568163e-02 -2.06193715e-01 -3.05936605e-01
-3.30521166e-02 -1.99945956e-01 -1.81736186e-01 1.07510857e-01
1.99526295e-01]]

So, do you use any methods to convert them?

ZCWzy · 2024-07-01T05:01:24Z

@TCN1214 The body_pose in 4D-humans is a rotation matrix representation and the body_pose in smpl_optimized_aligned_scale.npz is an axis angle representation (Axis with angle magnitude).
A similar question was asked in the 4D-humans issue. conversion from rotation matrix to axis angle
The converted body_pose seems to be "inaccurate", I checked it with a visualization tool and I think it's correct

TCN1214 · 2024-07-01T07:33:06Z

@ZCWzy, got it, thanks! And may I know if you also get the global_orient and betas from 4D Human? I noticed that in smpl_optimized_aligned_scale.npz , the betas are the same for all frames. Do you have any idea why?

ZCWzy · 2024-07-01T08:29:57Z

@ZCWzy, got it, thanks! And may I know if you also get the global_orient and betas from 4D Human? I noticed that in smpl_optimized_aligned_scale.npz , the betas are the same for all frames. Do you have any idea why?

4d-Humans tracking is demonstrated with the help of PHALP. The bbox, global_orient, body_pose and betas can be obtained directly using PHALP. But there's a problem with global_orient in this case, it doesn't work directly.

beta is used to describe the shape of the human body, and the authors may have assumed that the shape of the subject's body would not change

TCN1214 · 2024-07-01T09:59:02Z

4d-Humans tracking is demonstrated with the help of PHALP. The bbox, global_orient, body_pose and betas can be obtained directly using PHALP. But there's a problem with global_orient in this case, it doesn't work directly.

beta is used to describe the shape of the human body, and the authors may have assumed that the shape of the subject's body would not change

The betas produced from 4D human data are different for each frame. May I know how I can get the same betas for all frames?

ZCWzy · 2024-07-01T12:50:48Z

4d-Humans tracking is demonstrated with the help of PHALP. The bbox, global_orient, body_pose and betas can be obtained directly using PHALP. But there's a problem with global_orient in this case, it doesn't work directly.
beta is used to describe the shape of the human body, and the authors may have assumed that the shape of the subject's body would not change

The betas produced from 4D human data are different for each frame. May I know how I can get the same betas for all frames?

I simply averaged

TCN1214 · 2024-07-01T16:10:13Z

I simply averaged

Alright, I got it. Thanks for your answer!

TCN1214 · 2024-07-06T05:41:24Z

Hi, @ZCWzy, you mentioned that scale and translation come from alignments.npy right? But when I compare it, I noticed that the translation values in smpl_optimized_aligned_scale.npz are different from alignment.npy,
the translation values I extracted from smpl_optimized_aligned_scale.npz is quite large,
[[-10.122043 1.2542624 7.5895085 ]]
In contrast, the translation values I obtained from alignment.npy is much smaller,
[[-0.0011, 0.0050, -0.0378]].

And also, the scale I obtained from alignment.npy is also different from the smpl_optimized_aligned_scale.npz,
Example
The scale I directly extracted from smpl_optimized_aligned_scale.npz (parkinglot from Neuman dataset)
[3.965441 3.9393816 3.935972 3.932178 3.9514518 4.013123 3.6961203
3.6949155 3.6817148 3.69526 3.67739 3.6596239 3.602954 3.596763
3.601293 3.538034 3.6467068 3.653771 3.6413672 3.6631591 3.6483827
3.6039793 3.7009943 3.6978316 3.6661446 3.7132351 3.7439525 3.7223043
3.7446861 3.7167091 3.718121 3.7254384 3.7595584 3.698107 3.6785161
3.7461946 3.6378756 3.7673268 3.9743676 3.9388347 3.9378428 3.840855 ]

The scale I obtained from alignment.npy (parkinglot from Neuman dataset)
[3.53917746 3.47164602 3.43697076 3.45280097 3.46665239 3.54570767
3.5541983 3.49975832 3.46148575 3.50300569 3.53204423 3.53689076
3.61704864 3.57576852 3.55589865 3.5947892 3.70504546 3.57626152
3.58610565 3.57432691 3.59437816 3.56360463 3.63913091 3.60963319
3.60541642 3.6163094 3.55629385 3.60355709 3.62477594 3.63914295
3.58286411 3.63471325 3.62164655 3.63289107 3.59772363 3.6078752
3.567438 3.61177402 3.72219769 3.58786174 3.69255453 3.65207427]

So could you tell me more details about how you get the scales and translation? Do you just run alignment.npy or do you add some modifications?

ZCWzy · 2024-07-06T10:42:51Z

@TCN1214

I think transl is getting it from here.
~~I verified it using blender.（Transform the original vertices using the scale and transl I calculated, it is only a rotation away from the vertices in the world coordinate system）~~
This transl is not quite right, I should separate rotation, translation and scale from that transf matrix.

scale has a more accurate way of fetching（from neuman/export_alignment）Numerical errors are quite normal. Every time you run export_alignment.py the scale is different

TCN1214 · 2024-07-06T15:43:54Z

Alright, thank you for your help!

TCN1214 · 2024-07-07T06:02:41Z

I think transl is getting it from here.
~~I verified it using blender.（Transform the original vertices using the scale and transl I calculated, it is only a rotation away from the vertices in the world coordinate system）~~
This transl is not quite right, I should separate rotation, translation and scale from that transf matrix.

@ZCWzy May I know how you separate them?

ZCWzy · 2024-07-07T07:54:47Z

I think transl is getting it from here.
~~I verified it using blender.（Transform the original vertices using the scale and transl I calculated, it is only a rotation away from the vertices in the world coordinate system）~~
This transl is not quite right, I should separate rotation, translation and scale from that transf matrix.

@ZCWzy May I know how you separate them?

I'm not sure because I don't multi view geometry. You should refer to neuman/export_alignment.py, as well as the relevant papers to be sure

TCN1214 · 2024-07-07T09:25:16Z

I'm not sure because I don't multi view geometry. You should refer to neuman/export_alignment.py, as well as the relevant papers to be sure

Alright, I'll look into them. Thank you for your answer!

TCN1214 · 2024-07-08T04:49:17Z

for vert, vertInWorld, scale, translation, go in zip(verts, vertsInWorld, scales, translations, pose):
vertWithoutRotation = vert*scale +translation

    rotation = open3d_registration(vertWithoutRotation, vertInWorld)

    # 计算旋转矩阵
    rotation = rotation[:3, :3]
    
    go = angle_axis_to_quaternion(torch.tensor(go)) #轴角转四元数
    go = quaternion_to_rotation_matrix(go) #四元数转旋转矩阵
    go=np.array(go)
    tmp = rotation @ go
    tmp=torch.tensor(tmp)
    tmp=rotation_matrix_to_angle_axis(tmp)
    tmp=np.array(tmp)

    global_orient.append(tmp)

@ZCWzy may I know what is your structure of the open3d_registration function, and what is it used for?

ZCWzy · 2024-07-08T07:06:28Z

for vert, vertInWorld, scale, translation, go in zip(verts, vertsInWorld, scales, translations, pose):
vertWithoutRotation = vert*scale +translation
    rotation = open3d_registration(vertWithoutRotation, vertInWorld)

    # 计算旋转矩阵
    rotation = rotation[:3, :3]
    
    go = angle_axis_to_quaternion(torch.tensor(go)) #轴角转四元数
    go = quaternion_to_rotation_matrix(go) #四元数转旋转矩阵
    go=np.array(go)
    tmp = rotation @ go
    tmp=torch.tensor(tmp)
    tmp=rotation_matrix_to_angle_axis(tmp)
    tmp=np.array(tmp)

    global_orient.append(tmp)
@ZCWzy may I know what is your structure of the open3d_registration function, and what is it used for?

open3d_registration is a method of the open source library open3d for the purpose of point cloud alignment. Here I want to move the original verts (which is a point cloud of 6890 points) to verts_world by scaling and translating it, so that the two point clouds are only rotated. In this way the global_orient is corrected to the correct position. But there seems to be a problem with this line of thinking, ~~because my translations are not accurate enough~~ I entered it wrong, the transl is quite accurate. Why is the rotation wrong?

TCN1214 · 2024-07-09T03:19:41Z

open3d_registration is a method of the open source library open3d for the purpose of point cloud alignment. Here I want to move the original verts (which is a point cloud of 6890 points) to verts_world by scaling and translating it, so that the two point clouds are only rotated. In this way the global_orient is corrected to the correct position. But there seems to be a problem with this line of thinking, ~~because my translations are not accurate enough~~ I entered it wrong, the transl is quite accurate. Why is the rotation wrong?

@ZCWzy Sorry, I'm not sure yet.... Now I'm trying your method to generate the global_orient. May I know where I can refer to the code of open3d_registration function?

ZCWzy · 2024-07-09T03:37:52Z

@TCN1214
do not use that way to create global_orient, it may not accurent.

use this:

def solve_transformation(verts, j3d, j2d, plane_model, colmap_cap, smpl_cap):
    np.set_printoptions(suppress=True)
    mvp = np.matmul(smpl_cap.intrinsic_matrix, smpl_cap.extrinsic_matrix)
    trans = solve_translation(j3d, j2d, mvp)
    smpl_cap.cam_pose.camera_center_in_world -= trans[0]
    joints_world = (ray_utils.to_homogeneous(
        j3d) @ smpl_cap.cam_pose.world_to_camera.T @ colmap_cap.cam_pose.camera_to_world.T)[:, :3]
    scale = solve_scale(joints_world, colmap_cap, plane_model)

    transf = smpl_cap.cam_pose.world_to_camera.T * scale
    transf[3, 3] = 1
    transf = transf @ colmap_cap.cam_pose.camera_to_world_3x4.T

    rotation = smpl_cap.cam_pose.world_to_camera.T
    rotation[3, 3] = 1
    rotation = rotation @ colmap_cap.cam_pose.camera_to_world.T
    rotation = rotation[:3,:3]

    verts_world = ray_utils.to_homogeneous(verts) @ transf
    return transf, verts_world,rotation

then

    pose = []
    file = joblib.load("smpl_output_romp.pkl")
    file = file[1]['pose']
    for it in file:
        go = [it[0], it[1], it[2]]
        pose.append(go)

    global_orient = []
    for go, rotation in zip(pose, rotations):
        go = angle_axis_to_quaternion(torch.tensor(go))  # 轴角转四元数
        go = quaternion_to_rotation_matrix(go)  # 四元数转旋转矩阵
        go = np.array(go)

        tmp = rotation.T @ go
        tmp = torch.tensor(tmp)
        tmp = rotation_matrix_to_angle_axis(tmp)
        tmp = np.array(tmp)

        global_orient.append(tmp)

That seems about right.

But it would make the
assert x.max() <= 1 + EPS and x.min() >= -EPS, f "x must be in [0, 1], got {x.min()} and {x.max()}"
(from /hugs/models/models/triplane.py)
The restriction is not satisfied. Why？

TCN1214 · 2024-07-09T03:55:40Z

@ZCWzy You mean you have an error, right? Can I have a look at your error?

TCN1214 · 2024-07-09T07:03:56Z

But it would make the
assert x.max() <= 1 + EPS and x.min() >= -EPS, f "x must be in [0, 1], got {x.min()} and {x.max()}"
(from /hugs/models/models/triplane.py)
The restriction is not satisfied. Why？

@ZCWzy I have encountered this error before. The reason I didn't satisfy the condition assert x.max() <= 1 + EPS and x.min() >= -EPS, f "x must be in [0, 1], got {x.min()} and {x.max()}" was because I used the wrong scale and translation. When I used the translation and scale values from alignments.npy, the problem was solved. However, I don't think the reason you're getting this error is the same as mine, because you used the translation and scale values from alignments.npy, right?

ZCWzy · 2024-07-09T08:46:39Z

But it would make the
assert x.max() <= 1 + EPS and x.min() >= -EPS, f "x must be in [0, 1], got {x.min()} and {x.max()}"
(from /hugs/models/models/triplane.py)
The restriction is not satisfied. Why？

@ZCWzy I have encountered this error before. The reason I didn't satisfy the condition assert x.max() <= 1 + EPS and x.min() >= -EPS, f "x must be in [0, 1], got {x.min()} and {x.max()}" was because I used the wrong scale and translation. When I used the translation and scale values from alignments.npy, the problem was solved. However, I don't think the reason you're getting this error is the same as mine, because you used the translation and scale values from alignments.npy, right?

I remove this assert then train. It looks like there's no problem.

TCN1214 · 2024-07-09T12:16:36Z

You can use the colab provided by 4D-humans.
!python track.py video.source=(your images path)

@ZCWzy When I run the command !python track.py video.source=(your images path) in the Colab provided by 4D Humans, I get this error:

[12/03 09:12:56] INFO No OpenGL_accelerate module loaded: No module

When I downloaded OpenGL_accelerate, I get this error below:

[07/09 10:29:53] INFO OpenGL_accelerate module loaded

This is why?

ZCWzy · 2024-07-09T12:43:26Z

You can use the colab provided by 4D-humans.
!python track.py video.source=(your images path)

@ZCWzy When I run the command !python track.py video.source=(your images path) in the Colab provided by 4D Humans, I get this error:

[12/03 09:12:56] INFO No OpenGL_accelerate module loaded: No module

When I downloaded OpenGL_accelerate, I get this error below:

[07/09 10:29:53] INFO OpenGL_accelerate module loaded

This is why?

put basicModel_neutral_lbs_10_207_0_v1.0.0.pkl in /4D-humans/data and /4D-humans
from this issue

TCN1214 · 2024-07-09T13:40:28Z

put basicModel_neutral_lbs_10_207_0_v1.0.0.pkl in /4D-humans/data and /4D-humans
from this issue

@ZCWzy Yeah, I put the model already, but It still has this error (INFO No OpenGL_accelerate module loaded: No module), so I think the problem is not the SMPL model. Are you able to run the colab provided by 4D humans?

ZCWzy · 2024-07-10T01:38:43Z

put basicModel_neutral_lbs_10_207_0_v1.0.0.pkl in /4D-humans/data and /4D-humans
from this issue

@ZCWzy Yeah, I put the model already, but It still has this error (INFO No OpenGL_accelerate module loaded: No module), so I think the problem is not the SMPL model. Are you able to run the colab provided by 4D humans?

https://colab.research.google.com/drive/11XZBoaMfa28y874r4B7VSVokNFlXiqqz?usp=sharing
After running track, I see "INFO OpenGL_accelerate module loaded", so I need to wait about 6-10 minutes to get the pkl.
You can tell if it's running by looking at the colab resource usage.

yashgarg98 · 2024-07-17T21:20:22Z

Hey, @ZCWzy after the alignment, were you able to fix the global coordinate issue? Also are you able to generate the human gaussian splat using HUGS?

Daydreamer-f · 2024-07-18T07:40:54Z

Hi @ZCWzy, I am currently encountering some difficulties while preprocessing my dataset, specifically with the 4dhuman folder. I noticed that you successfully processed your data. Could you please share your preprocessing code?
I would also appreciate the opportunity to discuss further with you privately, as I believe we might have similar research interests. Here is my email (ysfang0306@gmail.com), feel free to reach out! Thank you very much!

ZCWzy · 2024-07-18T09:10:17Z

Hey, @ZCWzy after the alignment, were you able to fix the global coordinate issue? Also are you able to generate the human gaussian splat using HUGS?

Yeah, but my rebuild didn't work out so well.

ZCWzy · 2024-07-18T09:17:01Z

Hi @ZCWzy, I am currently encountering some difficulties while preprocessing my dataset, specifically with the 4dhuman folder. I noticed that you successfully processed your data. Could you please share your preprocessing code? I would also appreciate the opportunity to discuss further with you privately, as I believe we might have similar research interests. Here is my email (ysfang0306@gmail.com), feel free to reach out! Thank you very much!

这位北大的✌好，请参照我之前的回复产生自定义数据。bbox，bodypose，betas都很容易利用4d-humans生成。global_orient，scale和transl需要轻微魔改一下neuman的预处理代码。这个数据预处理和转换不难写的
实际上我想写一个自己的预处理过程，使用4d-human来替代romp，但是我不能把4d-human，或者phalp产生的j2d投影到输入的images上，因此不能准确产生后面三个参数

yashgarg98 · 2024-07-22T09:11:41Z

Hey, @ZCWzy after the alignment, were you able to fix the global coordinate issue? Also are you able to generate the human gaussian splat using HUGS?

Yeah, but my rebuild didn't work out so well.

Can you share some of the images that you reconstructed using your data to check the quality of the images?

ZCWzy · 2024-07-22T11:34:02Z

Hey, @ZCWzy after the alignment, were you able to fix the global coordinate issue? Also are you able to generate the human gaussian splat using HUGS?

Yeah, but my rebuild didn't work out so well.

Can you share some of the images that you reconstructed using your data to check the quality of the images?

My workstation was sent in for repair and I can't offer it at the moment. I have a feeling that the face alignment accuracy of the model isn't quite accurate enough, or that the quality of my images is too poor

Hualiang789 · 2024-08-17T14:29:43Z

@ZCWzy 你好兄弟，可以给个联系方式吗想问一下如何预处理自定义数据，并跑通它

ZCWzy · 2024-08-18T06:13:11Z

@ZCWzy 你好兄弟，可以给个联系方式吗想问一下如何预处理自定义数据，并跑通它

看我新仓库（）

hanswolff mentioned this issue Jul 12, 2024

How to perform HUGS on custom dataset? #12

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Inquiry About "4d_humans" Folder in NeuMan Dataset #6

Inquiry About "4d_humans" Folder in NeuMan Dataset #6

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