GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization, 2023
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 6representative citing papers
LRM is a large transformer that predicts a NeRF directly from a single image after training on a million-object multi-view dataset.
DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.
BoostDream refines coarse feed-forward text-to-3D assets via 3D distillation, multi-view SDS loss from a 2D diffusion model, and prompt-consistent normal maps to produce higher-quality results more efficiently than standard SDS.
SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.
Zero123++ produces high-quality 3D-consistent multi-view images from a single input by fine-tuning Stable Diffusion with targeted conditioning and training methods.
citing papers explorer
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GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction
GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
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LRM: Large Reconstruction Model for Single Image to 3D
LRM is a large transformer that predicts a NeRF directly from a single image after training on a million-object multi-view dataset.
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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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BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion
BoostDream refines coarse feed-forward text-to-3D assets via 3D distillation, multi-view SDS loss from a 2D diffusion model, and prompt-consistent normal maps to produce higher-quality results more efficiently than standard SDS.
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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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Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
Zero123++ produces high-quality 3D-consistent multi-view images from a single input by fine-tuning Stable Diffusion with targeted conditioning and training methods.