dreamgaussian

Maintainer: adirik

Total Score

9

Last updated 5/21/2024
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API SpecView on Replicate
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Paper LinkView on Arxiv

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Model overview

DreamGaussian is a generative AI model that uses Gaussian Splatting to efficiently create 3D content. Developed by the Replicate creator adirik, it builds on similar text-to-image and image-to-image models like StyleMC, GFPGAN, and Real-ESRGAN. Unlike those models focused on 2D image generation and enhancement, DreamGaussian aims to efficiently create 3D content from text prompts or input images.

Model inputs and outputs

DreamGaussian takes in either a text prompt or an input image, along with some additional parameters, and generates a 3D output. The input can be an image, a text description, or both. The model then samples points and renders them using Gaussian splatting to efficiently create a 3D object.

Inputs

  • Text: A text prompt to describe the 3D object to generate
  • Image: An input image to convert to 3D
  • Elevation: The elevation angle of the input image
  • Num Steps: The number of iterations to run the generation process
  • Image Size: The target size for the preprocessed input image
  • Num Point Samples: The number of points to sample for the Gaussian Splatting
  • Num Refinement Steps: The number of refinement iterations to perform

Outputs

  • 3D Output: A 3D object generated from the input text, image, and parameters

Capabilities

DreamGaussian can efficiently generate 3D content from text prompts or input images using the Gaussian Splatting technique. This allows for faster 3D content creation compared to traditional methods. The model can be used to generate a wide variety of 3D objects, from simple geometric shapes to complex organic forms.

What can I use it for?

DreamGaussian can be used for a variety of 3D content creation tasks, such as generating 3D assets for games, virtual environments, or product design. The efficient nature of the Gaussian Splatting approach makes it well-suited for rapid prototyping and iteration. Additionally, the model could be used to convert 2D images into 3D scenes, enabling new possibilities for 3D visualization and modeling.

Things to try

Experiment with different text prompts and input images to see the range of 3D objects DreamGaussian can generate. Try varying the input parameters, such as the number of steps, point samples, and refinement iterations, to find the optimal settings for your use case. Additionally, consider combining DreamGaussian with other AI models, such as LLAVA-13B or AbsoluteReality-v1.8.1, to explore more advanced 3D content creation workflows.



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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