PatternGSL introduces a learnable specification language for sewing patterns that lets vision-language models reconstruct explicit, simulation-ready 3D garments from single images, backed by a new 300K paired dataset.
arXiv preprint arXiv:2211.04604 , year=
4 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
4
Pith papers citing it
3
external citations · external index
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
HetScene proposes a two-stage heterogeneous diffusion framework that decomposes scenes into primary structural objects and secondary contextual objects to generate denser, more plausible indoor layouts.
3D Diffuser Actor unifies diffusion policies with 3D scene features to set new state-of-the-art results on RLBench and CALVIN robot benchmarks.
Augmenting robot datasets via diffusion-based semantic inpainting enables manipulation policies to solve unseen tasks with new objects and improves robustness to novel distractors.
citing papers explorer
-
PatternGSL: A Structured Specification Language for Template-Free and Simulation-Ready 3D Garments
PatternGSL introduces a learnable specification language for sewing patterns that lets vision-language models reconstruct explicit, simulation-ready 3D garments from single images, backed by a new 300K paired dataset.
-
HetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation
HetScene proposes a two-stage heterogeneous diffusion framework that decomposes scenes into primary structural objects and secondary contextual objects to generate denser, more plausible indoor layouts.
-
3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
3D Diffuser Actor unifies diffusion policies with 3D scene features to set new state-of-the-art results on RLBench and CALVIN robot benchmarks.
-
Scaling Robot Learning with Semantically Imagined Experience
Augmenting robot datasets via diffusion-based semantic inpainting enables manipulation policies to solve unseen tasks with new objects and improves robustness to novel distractors.