1.03D Asset Generation: Generating Action Primitiveshttps://mscvprojects.ri.cmu.edu/2026teamf3Method - 3D Asset Generation: Generating Action Primitivesrich600338<blockquote class="wp-embedded-content" data-secret="WWo7Apv2Pv"><a href="https://mscvprojects.ri.cmu.edu/2026teamf3/method/">Method</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2026teamf3/method/embed/#?secret=WWo7Apv2Pv" width="600" height="338" title="“Method” — 3D Asset Generation: Generating Action Primitives" data-secret="WWo7Apv2Pv" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
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Method Non-rigging Our method aims to learn an optimal canonical mesh M* that could correctly deform at each timestep. We achieve this by optimizing the vertex positions of the canonical mesh to minimize both geometric loss and photometric loss. We use the frozen ActionMesh’s deformation decoder to predict per-timestep deformation. For geometric loss, we first […]https://mscvprojects.ri.cmu.edu/2026teamf3/wp-content/uploads/sites/145/2026/05/img4.png1095291