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Your equation in Appendix B.3 states that the sigma vector is linearly spaced between omega_min and omega_max.
However, your code in src/training/motion.py here implements frequencies as log-linearly spaced:
src/training/motion.py
def construct_linspaced_frequencies(num_freqs: int, min_period_len: int, max_period_len: int) -> torch.Tensor: freqs = 2 * np.pi / (2 ** np.linspace(np.log2(min_period_len), np.log2(max_period_len), num_freqs)) # [num_freqs] freqs = torch.from_numpy(freqs[::-1].copy().astype(np.float32)).unsqueeze(0) # [1, num_freqs] return freqs
as the generated sequence satisfies that sigma_{i+1}/sigma_i = C
From my perspective, your code implementation makes more sense, so perhaps you should refine the paper?
The text was updated successfully, but these errors were encountered:
Hi @johannwyh , I apologize for not replying, I had quite some mess with my projects/deadlines and then lost this thread.
You are right about the error, thank you for point this out. We will update the paper.
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Your equation in Appendix B.3 states that the sigma vector is linearly spaced between omega_min and omega_max.
However, your code in
src/training/motion.py
here implements frequencies as log-linearly spaced:as the generated sequence satisfies that sigma_{i+1}/sigma_i = C
From my perspective, your code implementation makes more sense, so perhaps you should refine the paper?
The text was updated successfully, but these errors were encountered: