lizhe00 / animatablegaussians Goto Github PK

In the process of creating Avatar Animations, you may find that certain pose-driven animations deliver superior quality compared to others. For example, the "THuman4.0_POSE" may provide more convincing results than "MPI_mosh". How to improve the quality of driving any arbitrary action?

When I tried your method of processing camera parameters in the actorshq dataset and applied the processed extrinsic and intrinsic matrices to the original 3DGS, the results were very blurry. Have you tried the same method before？

How do I get smpl_params in my own dataset？

In line 52 of gaussian_renderer.py, camera_center = torch.linalg.inv(extr)[:3, 3] seems to be changed to camera_center = torch.linalg.inv(extr)[3, :3], although judging from the results It seems to have little impact.

Hi, thank you for the amazing work!

I just have a question regarding the inputs of the StyleUNet.

According to the paper, your StyleUNets takes as inputs both front and back posed position maps and outputs the front and back pose-dependent gaussian maps.

Meanwhile, I noticed that only the front posed position map is used as the condition to predict both front and back gaussian maps.

I wonder whether this is intentionally done since the outputs still look good.

Thank you in advance.

Hi,I wonder how long does the model take to train? I use one 3090 and have trained about 3 days...

Thank you for your excellent work and providing the codes.

I want to train on our own dataset. For the setting, how can I obtain smpl_params.npz? Should I use PyMAF-X as mentioned in the AvatarRex? If so, how can I tune the parameters to align it to the calibrated camera parameters (K[R|T])? I was curious about it because smpl parameters obtained from PyMAF-X are trained on fixed virtual camera (maybe focal length=(5000,5000) and extrinsic matrices are identity) across all the images.

I would appreciate it if you could answer.

Hi, thanks again for releasing the code! If I'd like to retrain the model to reduce the VRAM usage, what are some key parameters/configurations I can tune? Thanks!

Hello, is there a date expected to release the code?

Hi, thanks for your nice work. I have read your paper, but I did not find any information about the viewpoint settings during training and testing. I noticed that all 16 viewpoints were used during training in your code, but can you clarify which viewpoints were used during testing? For example, which viewpoints were the results in Table 2 of the paper based on? Could you provide some instructions regarding the frame and camera viewpoint configuration during testing?

Hello, is there a date expected to release the code?

This work looks amazing!!!

It looks extremely similar.

what about face movement

Getting this error for all files related to avatarrex_zzr

Hi, thanks for releasing the code! I'm wondering if it's possible to train/test with a batch size bigger than 1? What's the reason a batch size of 1 is used for training? Thanks!

I download your preprocessed files from [PREPROCESSED_DATASET.md].(https://github.com/lizhe00/AnimatableGaussians/blob/master/PREPROCESSED_DATASET.md)
Here is the detailed error:
Import AvatarNet from network.avatar
[ WARN:[email protected]] global /io/opencv/modules/imgcodecs/src/loadsave.cpp (239) findDecoder imread_('media/data4/yejr/AvatarReX/avatarrex_lbn1/smpl_pos_map/cano_smpl_pos_map.exr'): can't open/read file: check file path/integrity
Traceback (most recent call last):
File "main_avatar.py", line 829, in
trainer = AvatarTrainer(config.opt)
File "main_avatar.py", line 48, in init
self.avatar_net = AvatarNet(self.opt['model']).to(config.device)
File "/home/yejr/Digital_Avater/AnimatableGaussians-master/network/avatar.py", line 28, in init
self.cano_smpl_map = torch.from_numpy(cano_smpl_map).to(torch.float32).to(config.device)
TypeError: expected np.ndarray (got NoneType)
How should I do?

It is really a fantastic work. Congrats!
I managed to deal with all the data preprocessing and testing.
However I found the rendered results on avatar_lbn1 is not quite ideal, even on the training smpl dataset.
The rendered image rendered below ith command [python main_avatar.py -c configs/avatarrex_lbn1/avatar.yaml --mode=test]:


Is that sounds reasonable to you?
Thanks very much!

Error: No matching distribution found for igl==2.2.1

Description

When trying to install the required dependencies from the requirements.txt file, I encountered the following error:

Collecting glfw==2.4.0 (from -r requirements.txt (line 1))
Using cached glfw-2.4.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-manylinux2014_x86_64.whl.metadata (4.8 kB)
ERROR: Could not find a version that satisfies the requirement igl==2.2.1 (from versions: none)
ERROR: No matching distribution found for igl==2.2.1

This error occurs when trying to install the `igl` package at version 2.2.1, which appears to be listed in the `requirements.txt` file.

Environments Tested

I have tried the following environments:

1. venv with Python 3.11
2. Anaconda with Python 3.10
3. Anaconda with Python 3.9

Steps to Reproduce

1. Create a new virtual environment (using pyenv or Anaconda)
2. Install the required dependencies from the requirements.txt file

Expected Behavior

All the dependencies listed in the requirements.txt file should be installed successfully without any errors.

Additional Information

• cuda:12.2.0
• ubuntu22.04

Can you please let me know if you have an environment that works well for these errors?
Thank you in advance for your time.

How long the training is？

i want to try on another dataset