9 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.CV2024
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation
Ruikai Cui, Weizhe Liu, Weixuan Sun +9
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized c…
cs.CV2024★ 9 cited
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane
Han Yan, Yang Li, Zhennan Wu +9
We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass. Unlike existing methods that output a single, unified 3D s…
cs.CV2024★ 3 cited
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
Zhennan Wu, Yang Li, Han Yan +8
We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using dat…