Hello, Sir. I am trying to the test on 313 but i got error: FileNotFoundError: [Errno 2] No such file or directory: 'data/zju_mocap/CoreView_313\lbs\joints.npy'. Could you give me advice how to get joints.npy?

Hi, Thanks for your great work, I have some questions about the dataset:

1. The npy files in the new_vertices folder mean the SMPL mesh in the world coordinates, right? I wonder what unit they use, meter or millimeter?
2. I wonder if the provided smpl_param(pose parameters) are in the coordinate system defined in SMPL or in the world coordinates? In other words, if I use smplx to get mesh with provided params like: smpl_layer(pose, shape, th) will I get the same result in the vertices folder?

Hi, I'm having some problems implementing my own data with prepare_lbs_meta.py. I try to test the cope with CAPE dataset. The param of CAPE is

for k, v in cape_param.items():
    print(k)
    print(v.shape)

print: transl
        (3,)
        v_cano
        (6890, 3)
        pose
        (72,)
        v_posed
        (6890, 3)

I performed the following processing to fite prepare_lbs_meta.py.

vertices = cape_param["v_posed"].reshape(6890, 3)

shapes = betas.reshape(1, 10)
Th = cape_param["transl"].reshape(1, 3)
Rh = cape_param["pose"][:, :3].reshape(1, 3)
poses = cape_param["pose"].reshape(1, 72)
poses[:, :3] = 0
save_dict = {"shapes":shapes, "poses":poses, "Rh":Rh, "Th":Th, }

out_param = os.path.join(param_dir, f'{i:04d}')
out_verti = os.path.join(vertices_dir, f'{i:04d}')
np.save(out_param, save_dict)
np.save(out_verti, vertices)

But I find the result of get_tpose_blend_weights() seems not right

from left to right are the posed vertices, GT T-pose vertices, and process T-pose vertices. I simply checked the code against SMPL， there didn't seem to be any problems.

Am I doing it the right way? Can you give me some advice? Thanks

when loading human3.6m dataset, here comes a question that I can't change the batch_size from 1 to any other number > 1. Then I find out that it's because when loading data, the size of the param "pbw" changes as well. The pbw is loaded in the lbs/bweights folder, which is provided by you. I wanna process more other datasets, what should I do? How to make this pbw blend weights have the same size(n_batch,d,h,w,25)? @pengsida

Hi, Sida

I was trying to extract mesh results for sequences 392, 393, and 394 for ZJU-MoCap dataset in order to do some comparison. Since 392, 393, 394 do not come with preprocessed lbs folder I just run tools/prepare_blend_weights.py to generate folder. However when generating meshes it still says "lbs/bigpose_vertices.npy" is missing. Could you share these files for 392, 393, 394 or the script that can generate this "bigpose_vertices.npy"?

Thanks!

Congrats on this great work, and thanks a lot for open-sourcing this project!

1. I wonder why we need to retrain the blend weight fields of unseen human poses? It seems can directly use the Nerf and neural blend weight field for 3D animation.

2. And what‘s the 'Rh" mean? Is it the same of global_orient in SMPL?

   # transform smpl from the world coordinate to the smpl coordinate 
    params_path = os.path.join(self.data_root, cfg.params,
                               '{}.npy'.format(i))
    params = np.load(params_path, allow_pickle=True).item()
    Rh = params['Rh'].astype(np.float32)
    Th = params['Th'].astype(np.float32)
   

Hello I tried to run your code but I get error message... "Segmentation Fault (core dumped)"

I used pytorch==1.11.0, CUDA 11.3, torchvision 0.12.0..

Is your code only available in pytorch 1.4.0 & CUDA 10 version?

Hi,

I trained the AniSDF on ZJU-mocap 387 sequence with configs/sdf_pdf/anisdf_pdf_387.yaml. I then produce mesh with python run.py --type visualize --cfg_file configs/sdf_pdf/anisdf_pdf_387.yaml exp_name anisdf_pdf_387 vis_posed_mesh True resume True. However the reconstructed mesh seems noisy.

• Reconstructed mesh for 387 sequence

I found if I omit in lib/networks/renderer/sdf_mesh_renderer.py the following lines, the mesh is smooth.

if 'gradient_of_deformed_sdf' in dir(self.net):
    normal, sdf = self.batchify_normal_sdf(pts, batch)
    resd = -normal * sdf
    resd = torch.from_numpy(resd).to(pts)
    deformed_pts = pts + resd
else:
    deformed_pts = pts

• Reconstructed mesh without offset for mesh vertices.

Could you provide any suggestions to solve this problem?

Thanks a lot.

hello I read your paper in ICCV 2021 and newly pre-print paper. I am confused about the experiment settings. In paper "Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos": 1.Section 3.2.1 describe the method in the ICCV2021 paper and its corresponding config file is configs/aninerf_s9p.yaml 2.Section 3.2.2 's config file is configs/aligned_nerf_pdf/aligned_aninerf_pdf_s9p.yaml(termed pdf) 3.the config file of "pdf+sdf" is configs/sdf_pdf/anisdf_pdf_s9p.yaml am I right? and I dont know what the config file configs/aligned_nerf_lbw/aligned_aninerf_lbw_s9p.yaml refer to ...

python run.py --type visualize --cfg_file configs/aninerf_313.yaml exp_name aninerf_313 vis_posed_mesh True The following error occurs when running the above statement。 AttributeError: 'Network' object has no attribute 'get_alpha'

According to your readme, I directly run the training code for animatable sdf on human36. This reports an error, i.e., no such file or directory: 'data/h36m/S9/Posing/lbs/weights.npy'.

I don't find weights.npy in the corresponding directory under human36 datasets. Do I need to additionally generate this weights.npy ?

In current code, the funciton transform_can_smpl is missing. When I ran the code there was a missing function error and I didn't find it in this code base.

Hi, when I want to install the pointnet2 and PCPR libraries to run the code in your project, I met these problems:

1. Although I had installed the pointnet2 successfully, when I run the code, it occurred this error:

2. When I try to install the PCPR, I can't install it successfully.

How can I install them and run the code successfully? Thanks.