Comments (5)
Hi, it is great to hear that. We encode the 3D human with 3D Gaussians in a canonical space, and then convert the 3D Gaussians from the canonical space to the target space to perform the optimization.
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Thank you for your reply @skhu101 . But I still have a question: the 3D Gaussian distribution in different poses is different, and the optimization process is carried out after converting 3D Gaussians to the target pose, which is like a static Gaussian to construct a dynamic scene, do you find this feasible? And can you tell me which file you implemented this in the project?
Best wish
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Hi, very good question. 3D human has specific structures, so we can articulate a static Gaussian to different target poses through Linear Blending Skinning (LBS) transformation. The implementation code is in line 69 of gaussian_renderer/init.py. This idea is also explored in previous HumanNeRF methods.
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Sorry for taking so long to get back to you. I get what you mean: by transforming Gaussian of canonical pose to the target pose and then optimizing it. I understand correctly?
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Yes, you are right.
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