Hi3DGen is a cutting-edge framework designed to generate high-fidelity 3D geometry from single 2D images by leveraging a novel normal bridging technique. Traditional methods often struggle with accurately reproducing fine-grained geometric details due to domain gaps, lighting variations, and inherent ambiguities in RGB images. Hi3DGen addresses these challenges by decoupling the image-to-geometry generation into two stages: first estimating detailed normal maps from images using a noise-injected dual-stream network that stabilizes and sharpens predictions, and then converting these normal maps into 3D geometry through a normal-regularized latent diffusion model. This approach effectively bridges the gap between 2D images and 3D models, enabling the generation of rich, detailed, and consistent 3D shapes that closely align with the input visuals.
About Hi3DGen
The framework also includes a sophisticated 3D data synthesis pipeline that produces high-quality synthetic datasets with complex geometric structures and rich surface details to support robust model training. Extensive experiments demonstrate that Hi3DGen outperforms state-of-the-art image-to-3D generation methods in terms of geometric fidelity, surface detail richness, and consistency with input images. User studies involving both amateur users and professional 3D artists confirm that Hi3DGen’s outputs are highly rated for both everyday applications like 3D printing and professional uses such as modeling and design. The model produces smooth surfaces when input images lack detail while preserving sharpness and accuracy in more complex regions, showcasing its versatility and robustness.
Hi3DGen opens new possibilities across various industries including game and film production, virtual and augmented reality, e-commerce, 3D printing, and cultural heritage preservation by enabling rapid creation of high-quality 3D assets directly from photos. Users can interact with an online demo to upload images, generate 3D shapes, and export meshes in different formats for further use. While some limitations remain due to the generative nature of latent diffusion, such as occasional inconsistencies in details, ongoing research aims to enhance reconstruction-level accuracy. The complete codebase and training scripts are scheduled for open-source release, encouraging wider adoption and further innovation in high-fidelity 3D geometry generation from images.
Key features include:
Two-stage normal bridging approach for stable and sharp normal map estimation and detailed 3D geometry generation
Normal-regularized latent diffusion learning to enhance geometric fidelity
High-quality synthetic 3D data synthesis pipeline for robust training
Outperforms state-of-the-art methods in shape accuracy, detail richness, and image consistency
Supports mesh export in multiple formats for downstream applications
User-friendly online demo for image upload and 3D asset generation
Applicable to game development, VR/AR, e-commerce, 3D printing, and cultural heritage
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