Garment Particles is a 5D point cloud representation jointly encoding 2D sewing patterns and 3D geometry, supporting rectified flow generation from high-level inputs and diffusion-based editing of patterns or shapes.
Clr-wire: Towards continuous latent representations for 3d curve wireframe generation
3 Pith papers cite this work. Polarity classification is still indexing.
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FrameTwin aligns a curve-anchored Gaussian digital twin to sparse-view observations of deforming wireframes via differentiable rendering to enable adaptive trajectory updates in robotic 3D printing.
GPC learns a motion vocabulary via Finite Scalar Quantization and end-to-end RL, then trains an autoregressive transformer for next-token control generation, achieving 99.98% motion reproduction success with emergent robustness.
citing papers explorer
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Garment Particles: A 2D--3D Symmetric Garment Representation for Generation and Editing
Garment Particles is a 5D point cloud representation jointly encoding 2D sewing patterns and 3D geometry, supporting rectified flow generation from high-level inputs and diffusion-based editing of patterns or shapes.
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FrameTwin: Curve-Anchored Gaussian Alignment from Sparse Views for Adaptive Wireframe 3D Printing
FrameTwin aligns a curve-anchored Gaussian digital twin to sparse-view observations of deforming wireframes via differentiable rendering to enable adaptive trajectory updates in robotic 3D printing.
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GPC: Large-Scale Generative Pretraining for Transferable Motor Control
GPC learns a motion vocabulary via Finite Scalar Quantization and end-to-end RL, then trains an autoregressive transformer for next-token control generation, achieving 99.98% motion reproduction success with emergent robustness.