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.
ACM Transactions on Graphics , volume = 41, number = 4, pages =
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
A latent diffusion model over continuous implicit neural representations samples INR parameters from sparse keyframes to reconstruct plausible, smooth, and diverse motions while preserving keyframe accuracy.
HALO learns latent reduced-order models with Poincaré maps for hybrid locomotion dynamics, allowing Lyapunov-based regions of attraction to be lifted from latent space to the full-order system.
MultiMat shows multimodal large models plus constrained search produce higher-quality procedural material graphs than text-only baselines on a new production dataset.
AnySurf generates open, closed and hybrid 3D surfaces with accurate normals via directed-edge enhanced FDG-D, a lightweight DE-Adapter, ROS-FT post-training, and the Outfit3D dataset.
Proposes a Koopman-surrogate framework that turns cyclic animation synthesis into a structured quadratic program solved via KKT system under temporal periodicity.
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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Generative Motion In-betweening by Diffusion over Continuous Implicit Representations
A latent diffusion model over continuous implicit neural representations samples INR parameters from sparse keyframes to reconstruct plausible, smooth, and diverse motions while preserving keyframe accuracy.
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HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincar\'e Maps, and Regions of Attraction
HALO learns latent reduced-order models with Poincaré maps for hybrid locomotion dynamics, allowing Lyapunov-based regions of attraction to be lifted from latent space to the full-order system.
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MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models
MultiMat shows multimodal large models plus constrained search produce higher-quality procedural material graphs than text-only baselines on a new production dataset.
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AnySurf: Any Surface Generation with Directed Edge
AnySurf generates open, closed and hybrid 3D surfaces with accurate normals via directed-edge enhanced FDG-D, a lightweight DE-Adapter, ROS-FT post-training, and the Outfit3D dataset.
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Closing Trajectories: Equation-Free Cyclic Animation via Koopman Surrogates
Proposes a Koopman-surrogate framework that turns cyclic animation synthesis into a structured quadratic program solved via KKT system under temporal periodicity.