TriplaneGaussian

Maintainer: VAST-AI

Total Score

75

Last updated 5/28/2024

👨‍🏫

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The TriplaneGaussian model, developed by VAST-AI, enables fast 3D reconstruction from single-view images in a few seconds. It uses a hybrid Triplane-Gaussian 3D representation to achieve this. Similar models like TripoSR and LGM also leverage Gaussian Splatting for efficient 3D generation, while InstantMesh and Stable-Dreamfusion focus on 3D mesh generation from images or text.

Model inputs and outputs

The TriplaneGaussian model takes a single-view 2D image as input and generates a 3D reconstruction based on a hybrid Triplane-Gaussian representation. This allows for fast reconstruction in just a few seconds, making it suitable for applications that require real-time 3D content creation.

Inputs

  • Single-view 2D image

Outputs

  • 3D reconstruction based on a hybrid Triplane-Gaussian representation

Capabilities

The TriplaneGaussian model has been demonstrated to work well on images generated by Midjourney, as well as captured real-world images. It can generate 3D reconstructions from these inputs in a matter of seconds, making it a powerful tool for rapid 3D content creation.

What can I use it for?

The TriplaneGaussian model could be useful for a variety of applications that require fast 3D reconstruction from 2D inputs, such as 3D asset creation, virtual reality, and augmented reality. Its ability to work with both synthetic and real-world images makes it a versatile tool for both content creators and developers.

Things to try

Experimenting with the TriplaneGaussian model on a variety of 2D inputs, including both synthetic and real-world images, could yield interesting results and insights. Comparing its performance to similar models like TripoSR, LGM, InstantMesh, and Stable-Dreamfusion could also provide valuable insights into the strengths and limitations of each approach.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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