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Support differentiable forward kinematics with n-dof
joints
#473
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Hi is there any update on this? Ive been trying to use this to solve an IK problem for a human kinematic chain with lots of 2-dof and 3-dof joints (total is aroud 125). Ive got it to work using autodiff but its painfully slow, uses a lot of memory, and gradient doesnt flow back to the outer optimization loop. Since you've also made the analytical backwards propagation I was wondering if youre looking to add that to more than 1-dof as well? |
@juulie thanks for your interest! We currently haven't scoped to work on these features. However, @fantaosha may be able to give some pointers if you wanted to give it a try yourself and can share relevant parts of your current implementation -- we welcome community contributions! |
Awesome! To elaborate on my problem: My neural model works on animated 3d markers that act as constraints on a human kinematic model. I want to use the Levenberg Marquardt optimizer to optimize the following error function:
fk_dof takes the dof_input tensor and uses a mapping to map each single dof_input entry to its correct local transformation and composes them. It then goes down the kinematic chain: For each joint it takes its parent global_transforms, applies the pre_translation and pre_rotation, and then the local SE3 transforms, in the right order. I feel like a custom backwards function could significantly improve the performance, especially since my human model has 123 dof. Its just that my knowledge on Lie algebra is lacking. Some more notes: |
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