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emperordali.bsky.social
David
@emperordali.bsky.social
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ML for math, video games and biology
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vincentzaraek.bsky.social
💡 Did you know? Neural network libraries like PyTorch can act as general-purpose approximators without building a network.

Just optimize a tensor with losses to solve tasks such a mesh matching with constraints. Continue on🧵

#MachineLearning
#NeuralNetworks
#TechInnovation
#Rigging
#CharacterTD
Neural networks can be powerful tools for approximating mesh deformations by aligning a source mesh to a target mesh One key advantage is the flexibility to include various constraints via loss functions such as: NORMAL LOSS: Preserve source normals to maintain the overall shape of the mesh. EDGE LOSS: Preserve edge lengths to retain the original volume of the mesh SYMMETRY LOSS: If the source mesh has symmetry, can penalizes asymmetrical deformations. LAPLACIAN LOSS: minimizes differences in vertex neighborhoods, preventing unwanted distortions
December 3, 2024 at 10:05 PM