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GaussianAnything - a 3D generative framework developed by Nanyang Technological University's S-Lab and Shanghai AI Lab, among others.

GaussianAnything is a 3D generative framework developed by Nanyang Technological University's S-Lab in collaboration with the Shanghai AI Lab and other institutions. GaussianAnything is based on an interactive point cloud structured latent space and a cascaded flow matching model...

What is GaussianAnything?

GaussianAnything is a 3D generation framework developed by Nanyang Technological University's S-Lab in collaboration with the Shanghai AI Lab and other institutions. Based on an interactive point cloud structured latent space and a cascaded flow matching model, GaussianAnything achieves high-quality, scalable 3D content generation. It supports multimodal conditional inputs, including point clouds, text, and single/multi-view images, and can generate 3D assets with geometry-texture decoupling for easier subsequent editing. GaussianAnything outperforms existing methods in both text-guided and image-guided 3D generation tasks, demonstrating better 3D consistency and generation results.

GaussianAnything's main functions

  • Multimodal conditional inputIt supports multiple input formats, including point clouds, text descriptions, and single/multi-view images.
  • High-quality 3D generationGenerates 3D models with rich details and high-quality surfaces, supporting output at different resolutions and levels of detail.
  • Highly efficient 3D editing capabilitiesIt supports flexible editing of the generated 3D models, such as shape adjustment and texture replacement.
  • Supports multiple output formatsThe generated 3D model can be exported as a point cloud, a Surfel Gaussian surface, or a triangular mesh to meet the needs of different application scenarios.

GaussianAnything's technical principles

  • 3D VAE encoderUsing multi-view RGB-D (depth) and normal (Normal) rendering maps as input, a 3D-Attention Transformer encoder is used to compress 3D objects into a latent space structured by point clouds, preserving rich 3D geometric and texture information, reducing the dimensionality of the latent space, and improving training efficiency.
  • Point cloud structured latent spaceBased on Cross Attention, features are projected onto sparse 3D point clouds to form latent variables with structured point cloud, preserving the geometric information of 3D objects and supporting efficient training of 3D diffusion models.
  • Cascaded diffusion model:
    • Phase 1Generate sparse point clouds to determine the geometric layout of 3D objects.
    • Second stageSection: Generate texture details based on point cloud conditions to achieve decoupling between geometry and texture.
  • High-quality decoderThe latent variables of the point cloud are progressively upsampled into a high-resolution Gaussian surface using a 3D Transformer and an upsampling module, and finally decoded into a dense 3D model.

GaussianAnything's project address

Application scenarios of GaussianAnything

  • 3D Games and Film Special EffectsQuickly generate high-quality 3D models and simplify the content creation process.
  • Virtual Reality (VR) and Augmented Reality (AR)): Create virtual scenes and objects to enhance immersion.
  • Industrial Design and Product DevelopmentQuickly generate and edit 3D design prototypes based on text or images.
  • Cultural Heritage and Architectural VisualizationIt enables 3D reconstruction and restoration for use in digital preservation and display.
  • Robotics and AI TrainingGenerate 3D data for robot vision and AI model training.
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