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Found 9 papers, 4 papers with code
CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization
First, we design the coupled neural shape (CNS) representation for supporting 3D shape editing.
Make-A-Shape: a Ten-Million-scale 3D Shape Model
We then make the representation generatable by a diffusion model by devising the subband coefficients packing scheme to layout the representation in a low-resolution grid.
EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
This paper presents a new text-guided technique for generating 3D shapes.
CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration
This paper presents CLIPXPlore, a new framework that leverages a vision-language model to guide the exploration of the 3D shape space.
Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation
This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain.
Neural Wavelet-domain Diffusion for 3D Shape Generation
This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain.
Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology.
SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds.
