DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.
Textmesh: Generation of realistic 3d meshes from text prompts.arXiv preprint arXiv:2304.12439,
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SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.
MVDream is a multi-view diffusion model that functions as a generalizable 3D prior, enabling more consistent text-to-3D generation and few-shot 3D concept learning from 2D examples.
MOC-3D adds a semantic view-order constraint using CLIP monotonicity and a manifold-based feature continuity module on SPD Riemannian space to reduce macro-topological and micro-geometric inconsistencies in SDS-based text-to-3D generation.
citing papers explorer
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DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.
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SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.
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MVDream: Multi-view Diffusion for 3D Generation
MVDream is a multi-view diffusion model that functions as a generalizable 3D prior, enabling more consistent text-to-3D generation and few-shot 3D concept learning from 2D examples.
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MOC-3D: Manifold-Order Consistency for Text-to-3D Generation
MOC-3D adds a semantic view-order constraint using CLIP monotonicity and a manifold-based feature continuity module on SPD Riemannian space to reduce macro-topological and micro-geometric inconsistencies in SDS-based text-to-3D generation.