If I want to change an OBJ face, what are the requirements? Or is there a template for the face you use? Then you can create many faces through the template. I read other issues and learned that not all OBJ can be used. Does the number of vertices of the mesh need to be the same? Does the face size need to be the same?

This is a cool project.

Hi, I ran the custom audio expressions on your neutral mesh object and it ran well. I wanted to run the audio on my own custom (model)object files. I have created the object files for my person model. For this how do I generate the asset files - face_mean.npy, face_std forehead_mask and neck mask files? Are these files generated for the object file, or am i supposed to resize the object file to the 6172 dimension in order to use with the existing asset files? Thank you for your help in advance.

i have a new obj file with 6172 points from the default obj file, Q1:what is the meaning of the file face_mean and face_std and the two txt with smoothing ? Is the middle face and the hyperbole face ? Q2: how to make the face_mean and face_std and the smooth txt file?

Hello,

I am trying to train MeshTalk on the VOCA dataset, however, the loss value explodes if I use a learning rate 1e-4 or higher, and keeps oscillating in the range of 0.2 if I use a lower learning rate (this does not lead to realistic results). I was wondering what training parameters were used in the paper?

I am using the following parameters: no. of frames, T = 128 optimizer SGD with lr=9e-5 (at the moment), momentum=0.9, nesterov=True M_upper = 5 and M_lower = 5 batch_size = 16

Thanks for any help!

The mesh graph (.obj) multiface provided has almost 2000 faces less than the mesh by meshtalk (.obj). I wonder how to cope with it. Should I do some remeshing work to connect the isolated vertices together?

Incredible work！Thanks! I have a question on using diffrent obj model. I tried to use obj model file created by deca, but meet a error:

(meshtalk) ubuntu@ubuntu-X640-G30:/data/cx/GANs/meshtalk$ python animate_face.py --model_dir weights/pretrained_models --audio_file test.wav --output outputs --face_template myasset/mzd.obj /home/ubuntu/.local/lib/python3.8/site-packages/torchaudio/backend/utils.py:53: UserWarning: "sox" backend is being deprecated. The default backend will be changed to "sox_io" backend in 0.8.0 and "sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail. warnings.warn( load assets... load models... Loaded: weights/pretrained_models/vertex_unet.pkl Loaded: weights/pretrained_models/context_model.pkl Loaded: weights/pretrained_models/encoder.pkl animate face mesh... /home/ubuntu/.local/lib/python3.8/site-packages/torch/functional.py:515: UserWarning: stft will require the return_complex parameter be explicitly specified in a future PyTorch release. Use return_complex=False to preserve the current behavior or return_complex=True to return a complex output. (Triggered internally at /pytorch/aten/src/ATen/native/SpectralOps.cpp:653.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore /home/ubuntu/.local/lib/python3.8/site-packages/torch/functional.py:515: UserWarning: The function torch.rfft is deprecated and will be removed in a future PyTorch release. Use the new torch.fft module functions, instead, by importing torch.fft and calling torch.fft.fft or torch.fft.rfft. (Triggered internally at /pytorch/aten/src/ATen/native/SpectralOps.cpp:590.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore Traceback (most recent call last): File "animate_face.py", line 93, in geom = template_verts.cuda().view(1, 1, 6172, 3).expand(-1, T, -1, -1).contiguous() RuntimeError: shape '[1, 1, 6172, 3]' is invalid for input of size 15069

What should I do if I want to animate different obj files?

I find that the number of vertices from your given template object is different from what I downloaded from multiface dataset. Especially the details of the mouth are quite different, would you please share more information about the experiments?

Hello,

How to train the autoregressive model for inference? In the forward function, what would be the first expression_one_hot tensor? I understand subsequent inputs would be the labels output of previous timestep.

`def forward(self, expression_one_hot: th.Tensor, audio_code: th.Tensor):

   x = self.embedding(expression_one_hot)

    for layer in self.context_layers:
        x = layer(x, audio_code)
        x = F.leaky_relu(x, 0.2)

    logits = self.logits(x)
    logprobs = F.log_softmax(logits, dim=-1)
    probs = F.softmax(logprobs, dim=-1)
    labels = th.argmax(logprobs, dim=-1)

    return {"logprobs": logprobs, "probs": probs, "labels": labels}` 

Thanks

Hi. I am very impressed with your wonderful research. Thank you so much for sharing the great results. I want to render a texture to the output generated by this model. Can I get a uv texture mapping file that matches the output?

Hi, I found the audio preprocessing use simple transformation in your codes (load_audio & audio_chunking). But there are different from your statement in paper where the paper says"Our audio data is recorded at 16kHz. For each tracked mesh, we compute the Mel spectrogram of a 600ms audio snippet starting 500ms before and ending 100ms after the respective visual frame. We extract 80-dimensional Mel spectral features every 10ms, using 1, 024 frequency bins and a window size of 800 for the underlying Fourier transform."

I didn't find any Mel spectral calculation in your code, why there are different? Is the current version is better than Mel spectral features?

I try to build pytorch3d 0.4.0 source with torch1.10 as same version as readme. But it always failed. The log is below:

/home/local/gcc-5.3.0/bin/gcc -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -DTHRUST_IGNORE_CUB_VERSION_CHECK -I/home/Projects/github_projects/pytorch3d/pytorch3d/csrc -I/home/software_packages/cub-1.10.0 -I/home/anaconda3/envs/torch1.10/lib/python3.7/site-packages/torch/include -I/home/anaconda3/envs/torch1.10/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/home/anaconda3/envs/torch1.10/lib/python3.7/site-packages/torch/include/TH -I/home/anaconda3/envs/torch1.10/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/home/anaconda3/envs/torch1.10/include/python3.7m -c /home/Projects/github_projects/pytorch3d/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp -o build/temp.linux-x86_64-3.7/home/Projects/github_projects/pytorch3d/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.o -std=c++14 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="_gcc" -DPYBIND11_STDLIB="_libstdcpp" -DPYBIND11_BUILD_ABI="_cxxabi1011" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0
  cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
  /home/Projects/github_projects/pytorch3d/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp: In function ‘std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor> RasterizeMeshesNaiveCpu(const at::Tensor&, const at::Tensor&, const at::Tensor&, const at::Tensor&, std::tuple<int, int>, float, int, bool, bool, bool)’:
  /home/Projects/github_projects/pytorch3d/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp:294:28: error: converting to ‘std::tuple<float, int, float, float, float, float>’ from initializer list would use explicit constructor ‘constexpr std::tuple< <template-parameter-1-1> >::tuple(_UElements&& ...) [with _UElements = {const float&, int&, const float&, const float&, const float&, const float&}; <template-parameter-2-2> = void; _Elements = {float, int, float, float, float, float}]’
                 q[idx_top_k] = {
                              ^
  error: command '/home/local/gcc-5.3.0/bin/gcc' failed with exit status 1
  Building wheel for pytorch3d (setup.py) ... error
  ERROR: Failed building wheel for pytorch3d

Dose pytorch3d 0.4.0 really support torch1.10? I see the requirement is less than 1.7.1 in pytorch3d 0.4.0 url and less than 1.9.1 in pytorch3d main url

My environment:

• centos 7
• gcc 5.3.0
• cuda 10.2
• cub 1.10
• python 3.7 (conda environment)
• torch1.10
• pytorch3d 0.4.0

Hi! The paper mentions the following:

We release a subset of 16 subjects of this dataset and our model using only these subjects as a baseline to compare against

Since multiface was release with only 13 identities, can you please confirm what was used for the released pre-trained model? (e.g. the 13 identities in multiface? Those plus 3 other identities? Or another set of 16 identities?)

Thank you!