InstantMesh
Maintainer: TencentARC
107
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
InstantMesh is a feed-forward framework for efficient 3D mesh generation from a single image. It leverages the strengths of a multiview diffusion model and a sparse-view reconstruction model based on the LRM architecture to create diverse 3D assets quickly. By integrating a differentiable iso-surface extraction module, InstantMesh can directly optimize on the mesh representation to enhance training efficiency and exploit more geometric supervisions.
Compared to other image-to-3D baselines, InstantMesh demonstrates state-of-the-art generation quality and significant training scalability. It can generate 3D meshes within 10 seconds, making it a powerful tool for 3D content creation. The model is developed by TencentARC, a leading AI research group.
Model Inputs and Outputs
Inputs
- Single image
Outputs
- 3D mesh representation of the input image
Capabilities
InstantMesh can generate high-quality 3D meshes from a single image, outperforming other latest image-to-3D baselines both qualitatively and quantitatively. By leveraging efficient model architectures and optimization techniques, it can create diverse 3D assets within a short time, empowering both researchers and content creators.
What can I use it for?
InstantMesh can be a valuable tool for a variety of 3D content creation applications, such as game development, virtual reality, and visual effects. Its ability to generate 3D meshes from a single image can streamline the 3D modeling process and enable rapid prototyping. Content creators can use InstantMesh to quickly generate 3D assets for their projects, while researchers can explore its potential in areas like 3D scene understanding and reconstruction.
Things to try
Users can experiment with InstantMesh to generate 3D meshes from diverse input images and explore the model's versatility. Additionally, researchers can investigate ways to further improve the generation quality and efficiency of the model, potentially by incorporating additional geometric supervision or exploring alternative model architectures.
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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