TripoSR
TripoSR is an open source 3D generation model of Stability AI and VAST, which can generate high-quality 3D models from a single 2D picture in 0.5 seconds.
English
English
TripoSR is an open source 3D generation model jointly developed by Stability AI and VAST to provide the ability to quickly generate high-quality 3D models from single 2D images . The model is based on the Transformer architecture and adopts the principles of large-scale reconstruction models (LRM) , which significantly improves in speed and quality. The biggest highlight of TripoSR is its extremely fast generation speed - on the NVIDIA A100 GPU , it takes less than 0.5 seconds to generate high-quality 3D models from a 2D picture, greatly reducing the time and resource consumption required for traditional 3D modeling.
TripoSR is a MIT license that supports commercial, personal and research use and is one of the most powerful 3D reconstruction tools in the open source world. Whether in the fields of game development, film production, product design, architectural planning , or virtual reality (VR) and augmented reality (AR) , TripoSR has a wide range of application prospects.
Key features of TripoSR :
Generate 3D models for single image
TripoSR can automatically generate corresponding 3D models from a single 2D picture, identify objects in the picture, extract their shapes and features, and reconstruct the corresponding 3D geometric structure.
Quick Generation and High Quality Outputs Using the NVIDIA A100 GPU, TripoSR generates high-quality 3D models in less than 0.5 seconds, far faster than other traditional 3D reconstruction tools.
Adapting to multiple image types, whether it is static images or complex scene images, TripoSR can process and generate accurate 3D models.
The 3D model with high-quality rendering output reaches an excellent level of detail and realism, suitable for a variety of commercial and creative uses.
Technical principles of TripoSR :
TripoSR 's technical architecture is based on the Transformer architecture and neural radiation field (NeRF) model, and extracts the global and local features of the image through self-attention and cross-attention layers. Its image encoder uses the DINOv1 vision transformer model to convert images into potential vectors, providing key information for subsequent 3D reconstruction.
The three-plane-NeRF representation is one of the core innovations of TripoSR . The neural network built through multi-layer perceptron (MLP) stacking can accurately predict the color and density of objects, allowing TripoSR to make significant progress in fine modeling and texture reconstruction.
Technical Advantages :
Transformer architecture: efficiently process global and local information of images, improving the speed and quality of 3D reconstruction.
Three-plane neural radiation field: Improves the texture details and object surface modeling capabilities of 3D models.
Quick reasoning: The reasoning speed on the GPU is extremely fast, with a generation time of only 0.5 seconds.
High-quality reconstruction: both qualitative and quantitative evaluation results are superior to other existing open source solutions.
TripoSR application scenarios :
Game development: Accelerate game development by quickly converting 2D art pictures into 3D assets.
Movie & Animation: Generate 3D characters and scenes from static images for special effects and animation production.
Architectural design and urban planning: Rapidly generate 3D architectural models to improve visual effects.
Product Design and Prototyping: Transform 2D design into 3D models for product display and testing.
Virtual Reality (VR) and Augmented Reality (AR): Create 3D virtual objects and environments to enhance the VR/AR experience.
Education and training: 3D teaching models used in the field of education to improve interactive learning effects.
Get TripoSR :
Github Repository : TripoSR GitHub
HuggingFace Model Library : TripoSR on HuggingFace
arXiv Technical Paper : TripoSR Paper
Performance :
Quantitative results: TripoSR outperforms other methods on both Chamfer Distance (CD) and F-score (FS) metrics on multiple public data sets, achieving state-of-the-art performance levels.
Qualitative results: TripoSR is able to reconstruct object surface textures more carefully, providing higher quality 3D output.
Inference speed: On the NVIDIA A100 GPU , the generation time of each image of TripoSR is only 0.5 seconds , which is extremely efficient.
Quick Start :
Installation requirements :
Python >= 3.8
CUDA (if available)
PyTorch (refer to PyTorch Installation Guide )
Installation dependencies :
pip install -r requirements.txt
Running reasoning :
python run.py examples/chair.png --output-dir output/
Launch the Gradio application :
python gradio_app.py
