3D Generative Models CheatSheet

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1. Models (Open-Source)

• Shap-E: A conditional generative model from OpenAI that creates 3D assets from text prompts using a diffusion process.
• LLaMA-Mesh: This model unifies 3D mesh generation with language models, enabling the generation of 3D meshes from text prompts.
• Hunyuan3D-1: Hunyuan3D-1 is designed for generating high-quality 3D models and supports various applications in computer graphics and virtual environments.
• TRELLIS-Image-Large: his model focuses on generating detailed 3D representations from images, enhancing the fidelity of visual outputs in generative tasks.
• InstantMesh: InstantMesh is a tool for generating high-quality meshes from point clouds, facilitating efficient 3D modeling workflows.

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2. Inference Libraries / Toolkits

• Hunyuan3D-1 A Unified Framework for Text-to-3D and Image-to-3D Generation utilizing the Hunyuan3D-1 model effectively in various applications.
• InstantMesh: A library for creating high-quality meshes from point clouds, providing tools for mesh generation and manipulation.
• TripoSR: This toolkit focuses on super-resolution techniques for improving the quality of 3D models and images.
• TRELLIS: A comprehensive framework for working with generative models in 3D, offering various utilities for model inference and evaluation.
• dust3r: A library aimed at enhancing the generation of 3D structures through advanced algorithms and techniques.

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3. Datasets

• objaverse: A large-scale dataset containing diverse 3D object representations, useful for training generative models.
• TRELLIS-500K: A dataset of 500K 3D assets curated from Objaverse(XL), ABO, 3D-FUTURE, HSSD, and Toys4k, filtered based on aesthetic scores.
• Cap3D: A comprehensive dataset which contains multiple dataset and also it contains descriptive captions for 3D objects.

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4. Use Cases

• Gaming and Animation: Generating high-quality 3D assets for interactive applications and storytelling.
• Product Design: Rapid prototyping of design concepts using AI-generated 3D models.
• Education and Training: Creating 3D visualizations for educational content and simulations.
• Healthcare: Developing 3D anatomical models for diagnostics, training, and surgery planning.
• Virtual Reality (VR) and Augmented Reality (AR): Enhancing immersive experiences through dynamic 3D content creation.

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5. Deployment Options

• On-Premises Deployment: Running models on local servers for full control and data privacy.
• Cloud Services: Utilizing cloud providers like AWS, Azure, or Google Cloud for scalable deployment.
• Serverless GPU Platforms: Serverless GPU platforms like Inferless provide on-demand, scalable GPU resources for machine learning workloads, eliminating the need for infrastructure management and offering cost efficiency.
• Containerization: Using Docker or Kubernetes to manage and scale deployments efficiently.

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6. Training & Fine-Tuning Resources

• Machine Learning for 3D: An introductory course covering machine learning techniques applied to 3D data.
• Learning for 3D Vision: This course delves into the convergence of 3D vision and learning-based methods.
• 3D Point Cloud and Machine Learning: A video playlist detailing machine learning approaches specifically tailored to point cloud data.

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7. Evaluation & Benchmarking

• GT23D-Bench: A Comprehensive General Text-to-3D Generation Benchmark
• Peak Signal-to-Noise Ratio (PSNR): A critical metric used to evaluate the quality of reconstructions and ground-truth rendered images.
• Chamfer Distance (CD) and Fscore (FS): These two are standard metrics for evaluating the accuracy of 3D shape reconstructions.

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8. Model Optimization & Compression

• Quantization: Reducing model size for deployment on edge devices without significant loss of accuracy.
• Knowledge Distillation: Training smaller models to mimic larger, more complex models.
• Pruning: Removing redundant parameters to streamline model performance.

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9. Integration & Workflow Tools

• Meshgen: A Blender addon for generating meshes with AI.
• Open3D: An open-source library that supports the processing of 3D data, including visualization, reconstruction, and analysis functionalities.

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10. Common Challenges & Troubleshooting

• Data Quality: Ensuring high-quality input data for better outputs.
• Scalability: Managing computational resources for large-scale 3D generation.
• Model Robustness: Addressing failures in handling diverse input types.
• Interoperability Issues: Problems may arise when integrating AI tools with existing workflows. Leverage standard file formats and cross-platform libraries for smoother integration.
• Ethical Issues: Preventing misuse of generated models for unethical applications.

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11. Ethical Considerations

• Bias in Data: Ensuring diverse datasets to avoid biases in generated outputs.
• Intellectual Property (IP): Respecting copyright and IP laws when training or using generative models.
• Responsible Use: Establishing guidelines to prevent the misuse of generative technologies.
• Transparency: Maintain openness about how models are trained, evaluated, and deployed. This builds trust and promotes responsible AI usage.

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12. Licensing & Governance

• Check Licenses: (MIT, Apache 2.0, GPL) before commercial use.
• Hugging Face Model Cards: Follow best practices for transparency.
• Data Usage Agreements: Ensure compliance with dataset terms.