Text Encoded Extrusions (TEE) lets LLMs generate and edit manifold 3D meshes by learning sequences of face extrusions from decomposed quadrilateral meshes.
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4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
Fishbone introduces a unified rib-spine representation computed via adaptive heat method, iso-contour ribs, and geometry-aware spine that enables real-time parametric deformation, reduced-space simulation, and animation on general meshes.
UniRecGen unifies reconstruction and generation via shared canonical space and disentangled cooperative learning to produce complete, consistent 3D models from sparse views.
SpatialPrompt turns spatial sketches and voice prompts into executable constraints for controllable AI 3D generation in XR, enabling iterative collaborative creation with color-coded contributions.
citing papers explorer
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Learning to Build Shapes by Extrusion
Text Encoded Extrusions (TEE) lets LLMs generate and edit manifold 3D meshes by learning sequences of face extrusions from decomposed quadrilateral meshes.
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Fishbone: From One 3D Asset to a Million Controllable Edits
Fishbone introduces a unified rib-spine representation computed via adaptive heat method, iso-contour ribs, and geometry-aware spine that enables real-time parametric deformation, reduced-space simulation, and animation on general meshes.
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UniRecGen: Unifying Multi-View 3D Reconstruction and Generation
UniRecGen unifies reconstruction and generation via shared canonical space and disentangled cooperative learning to produce complete, consistent 3D models from sparse views.
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SpatialPrompt: XR-Based Spatial Intent Expression as Executable Constraints for AI Generative 3D Design
SpatialPrompt turns spatial sketches and voice prompts into executable constraints for controllable AI 3D generation in XR, enabling iterative collaborative creation with color-coded contributions.