AWS 3D Model Creation Pipeline Workshop Outline

Workshop Overview

This hands-on workshop guides participants through building a scalable 3D model creation pipeline using AWS services and open-source models. Participants will learn how to deploy and integrate text-to-image and image-to-3D workflows for games, media, and industrial applications.

Prerequisites

• AWS account access
• Basic understanding of AWS services
• Familiarity with containerization concepts
• Download and install Blender before the workshop begins

Module 1: Introduction to 3D Model Generation on AWS

Overview

• Introduction to text-to-image and image-to-3D workflows
• AWS services powering the pipeline (Amazon Bedrock, EKS, S3)
• Open-source models overview: TripoSR, Stable3DGen, Direct3D-S2
• Workshop architecture and components

Key Concepts

• Text-to-image generation using foundation models
• Image-to-3D conversion techniques
• Containerization of ML models
• Scalable inference on Kubernetes

Module 2: Exploring the Pre-Deployed Frontend UI

Hands-on Exercise

• Access the pre-deployed web interface
• Explore the text-to-image generation capabilities
• Test image-to-3D conversion with sample images
• Understand the API endpoints and data flow

Technical Components

• Frontend architecture overview
• API Gateway configuration
• Model routing and selection
• Asset storage and retrieval from S3

Module 3: Deploying TripoSR on Amazon EKS

Overview of TripoSR

• Introduction to TripoSR capabilities and architecture
• GitHub repository: https://github.com/VAST-AI-Research/TripoSR
• Model strengths and use cases

Hands-on Exercise

• Examine the containerized TripoSR model
• Deploy the pre-built container to EKS
• Configure scaling and resource allocation
• Test the deployment with sample inputs

Key Implementation Steps

1. Review the EKS cluster configuration
2. Understand the Kubernetes deployment manifests
3. Deploy the TripoSR container using kubectl
4. Configure API endpoints for the model
5. Test the integration with the frontend UI

Module 4: Integrating Stable3DGen with the Frontend

Overview of Stable3DGen

• Introduction to Stable3DGen capabilities and architecture
• GitHub repository: https://github.com/Stable-X/Stable3DGen
• Model strengths and use cases

Hands-on Exercise

• Containerize the Stable3DGen model
• Deploy to EKS cluster
• Configure API endpoints
• Integrate with the frontend UI
• Test end-to-end workflow

Key Implementation Steps

1. Build Docker container for Stable3DGen
2. Create Kubernetes deployment manifests
3. Deploy to EKS cluster
4. Configure API Gateway routes
5. Update frontend to support the new model
6. Test the integration

Module 5: Integrating Direct3D-S2 with the Frontend

Overview of Direct3D-S2

• Introduction to Direct3D-S2 capabilities and architecture
• GitHub repository: https://github.com/DreamTechAI/Direct3D-S2
• Model strengths and use cases

Hands-on Exercise

• Containerize the Direct3D-S2 model
• Deploy to EKS cluster
• Configure API endpoints
• Integrate with the frontend UI
• Test end-to-end workflow

Model Comparison and Selection

• Performance benchmarks across models
• Quality comparison for different use cases
• Resource requirements and scaling considerations
• Best practices for model selection

Module 6: Working with Generated 3D Assets in Blender

Hands-on Exercise

• Download GLB assets from the pipeline
• Import assets into Blender
• Basic asset modification techniques
• Texture and material adjustments
• Exporting optimized assets

Key Blender Techniques

• Importing GLB/GLTF files
• Mesh cleanup and optimization
• Material and texture adjustments
• Basic animation setup
• Export options for different platforms

Module 7: Production Scaling and Best Practices

Scaling Considerations

• EKS cluster scaling strategies
• Cost optimization for inference workloads
• Monitoring and observability setup
• Security best practices for model deployment

Production Readiness Checklist

• High availability configuration
• Disaster recovery planning
• CI/CD pipeline for model updates
• Performance optimization techniques

Module 8: Workshop Wrap-up and Action Items

Key Takeaways

• End-to-end 3D model creation pipeline architecture
• Open-source model integration techniques
• Scalable deployment strategies on AWS
• Post-processing workflows with Blender

Action Items for Participants

1. Technical Implementation Plan

   • Document requirements for implementing a similar pipeline
   • Identify integration points with existing systems
   • Estimate resource requirements and costs

2. Model Evaluation Framework

   • Create evaluation criteria for comparing different 3D models
   • Develop testing methodology for quality assurance
   • Establish performance benchmarks for specific use cases

3. Workflow Integration Strategy

   • Map the 3D asset pipeline to current content creation workflows
   • Identify bottlenecks that can be addressed with this technology
   • Plan for artist/developer training and adoption

4. Proof of Concept Development

   • Define scope for an initial implementation
   • Identify test cases specific to industry needs
   • Create timeline for POC deployment

5. AWS Architecture Review

   • Schedule a follow-up architecture review session with AWS Solutions Architects
   • Prepare specific questions about implementation details
   • Discuss customizations for specific industry needs

Workshop Resources

Code Repositories

• Workshop GitHub repository with sample code
• Model-specific repositories:
  • TripoSR: https://github.com/VAST-AI-Research/TripoSR
  • Stable3DGen: https://github.com/Stable-X/Stable3DGen
  • Direct3D-S2: https://github.com/DreamTechAI/Direct3D-S2

Documentation

• Workshop guide (PDF)
• AWS service documentation links
• Model documentation and papers
• Blender tutorials for 3D asset post-processing

AWS Resources

• CloudFormation templates for infrastructure deployment
• EKS cluster configuration examples
• API Gateway configuration samples
• Sample Lambda functions for model routing

Conclusion

By the end of this workshop, participants will have hands-on experience building a complete 3D model creation pipeline using AWS services and open-source models. They will understand how to deploy and scale inference workloads on EKS, integrate multiple models with a frontend UI, and process the generated assets using Blender. This knowledge will enable them to democratize 3D content creation and accelerate development cycles within their organizations.