3D Shape Generation
50 papers with code • 0 benchmarks • 1 datasets
Image: Mo et al
Benchmarks
These leaderboards are used to track progress in 3D Shape Generation
Combinatorial 3D Shape Dataset
Most implemented papers
Learning elementary structures for 3D shape generation and matching
TheoDEPRELLE/AtlasNetV2
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NeurIPS 2019
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
Combinatorial 3D Shape Generation via Sequential Assembly
To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework.
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
angeladai/cnncomplete
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CVPR 2017
Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis.
3D Point Capsule Networks
yongheng1991/3D-point-capsule-networks
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CVPR 2019
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.
ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation
liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation
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Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes.
LION: Latent Point Diffusion Models for 3D Shape Generation
nv-tlabs/LION
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To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes.
SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation
KAIST-Visual-AI-Group/SALAD
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ICCV 2023
We present a cascaded diffusion model based on a part-level implicit 3D representation.
DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation
liuzhengzhe/dreamstone-iss
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The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text.
TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
Specifically, we propose: 1) A large-scale rectified flow transformer for 3D shape generation, achieving state-of-the-art fidelity through training on extensive, high-quality data.
