Openlrm-large-obj-1.0 Open Source Model - Free and Fast Generation of 3D Models from a Single Image

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Openlrm Large Obj 1.0

Developed by zxhezexin

OpenLRM is the open-source implementation of the LRM paper, capable of generating 3D models from a single image.

3D Vision

Transformers

#Single Image to 3D #Tri-plane Decoding #High-resolution Rendering

Downloads 15

Release Time : 1/9/2024

Model Overview

OpenLRM is an open-source implementation for image-to-3D model conversion, supporting the generation of high-quality 3D models from a single input image.

Model Features

Multi-size Model Selection

Provides small, medium, and large model versions to accommodate different computational resource needs

High-quality 3D Reconstruction

Capable of generating detailed 3D models from a single image

Open-source Implementation

Fully open-source, facilitating research and secondary development

Model Capabilities

Single Image 3D Reconstruction

3D Model Generation

Image to 3D Conversion

Use Cases

3D Modeling

Rapid 3D Prototyping

Quickly generate 3D model prototypes from concept images

Significantly reduces 3D modeling workflow time

Digital Content Creation

Provides foundational 3D models for digital content creation such as games and films

Lowers the barrier to 3D asset creation

Education & Research

3D Reconstruction Research

Used for research in computer vision and graphics fields

🚀 Model Card for OpenLRM

This model card provides details about the OpenLRM project, an open - source implementation of the LRM paper.

🚀 Quick Start

This model card is for the OpenLRM project, which is an open - source implementation of the paper LRM.

📚 Documentation

Model Details

Property	Details
Model Type	openlrm - small - obj - 1.0, openlrm - base - obj - 1.0, openlrm - large - obj - 1.0, openlrm - small, openlrm - base, openlrm - large
Training Data	Objaverse, Objaverse + MVImgNet
Layers	12, 16
Feat. Dim	768, 1024
Trip. Dim.	32, 40, 80
Render Res.	192, 384
Link	[HF](https://huggingface.co/zxhezexin/openlrm - small - obj - 1.0), [HF](https://huggingface.co/zxhezexin/openlrm - base - obj - 1.0), [HF](https://huggingface.co/zxhezexin/openlrm - large - obj - 1.0), To be released

Model	Training Data	Layers	Feat. Dim	Trip. Dim.	Render Res.	Link
openlrm - small - obj - 1.0	Objaverse	12	768	32	192	[HF](https://huggingface.co/zxhezexin/openlrm - small - obj - 1.0)
openlrm - base - obj - 1.0	Objaverse	12	1024	40	192	[HF](https://huggingface.co/zxhezexin/openlrm - base - obj - 1.0)
openlrm - large - obj - 1.0	Objaverse	16	1024	80	384	[HF](https://huggingface.co/zxhezexin/openlrm - large - obj - 1.0)
openlrm - small	Objaverse + MVImgNet	12	768	32	192	To be released
openlrm - base	Objaverse + MVImgNet	12	1024	40	192	To be released
openlrm - large	Objaverse + MVImgNet	16	1024	80	384	To be released

Differences from the Original Paper

We do not use the deferred back - propagation technique in the original paper.
The triplane decoder contains 4 layers in our implementation.

📄 License

The model weights are released under the Creative Commons Attribution - NonCommercial 4.0 International License.
They are provided for research purposes only, and CANNOT be used commercially.

Disclaimer

This model is an open - source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors.

Ethical Considerations

⚠️ Important Note

This model should be used responsibly and ethically, and should not be used for malicious purposes.

Users should be aware of potential biases in the training data.

The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups.

Usage Considerations

💡 Usage Tip

The model is provided "as is" without warranty of any kind.

Users are responsible for ensuring that their use complies with all relevant laws and regulations.

The developers and contributors of this model are not liable for any damages or losses arising from the use of this model.

This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.

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