GenAI for 2D-to-3D Asset Generation
Paragon Corp · Posted 1 year ago
HealthcareGenerative AIConversational AI / Chatbots
- Budget
- USD 10,000
- Timeline
- Not specified
- Closed on
- 31 Dec 2025, 23:59 ICT
Problem statement
Campaigns increasingly demand dynamic and immersive visuals, but producing 3D assets from scratch remains expensive and time-consuming. Teams struggle to scale 3D asset creation across product types, scenes, and motion use cases.
Current 2D-to-3D tools have limited control, poor background variation, and often lack the capability to generate cinematic motion or video content. There’s a need for generative models (e.g., diffusion models) that can automate and scale 3D generation with creative flexibility.
Expected outcomes and success metrics
• Convert existing 2D visuals into high-quality 3D static images and motion clips
• Enable rapid prototyping of product shots, scenes, and campaign ideas
• Reduce 3D production time and cost by 50–80%
• Improve creative freedom and customization of 3D backgrounds and visual environments
• Support downstream usage in AR/VR, e-commerce, and digital campaign assets
Current process
Manual 3D modeling and rendering workflows
No scalable or automated pipeline for 2D-to-3D transformation
Limited 3D motion output or integration into brand campaigns
Current solution
• Manual 3D modeling and rendering workflows
• No scalable or automated pipeline for 2D-to-3D transformation
• Limited 3D motion output or integration into brand campaigns
Target users and impacted teams
• Visual Design & Motion Teams – for rendering and asset creation
• Campaign & Brand Teams – to experiment with formats and storytelling
• Innovation & Tech Creative Teams – to prototype immersive experiences
Data availability and readiness
• Product imagery and packaging shots available in high resolution
• Creative guidelines and scene composition templates exist
• Lack of clean 3D object datasets or 3D-aware prompt libraries
• No unified metadata tagging system for asset recall and reuse
Integration, deployment and infrastructure
• Preferred to integrate with:
- Digital asset libraries, scene builders, and animation tools
- WebGL-based 3D viewers for front-end usage
- Snowflake (internal data lake)
• Deployment: GPU-accelerated cloud platform preferred (e.g., AWS)
• Infrastructure:
- Support for diffusion models, controlnet, and 3D pose conditioning
- Render outputs to formats usable across campaign, AR/VR, and digital touchpoints
- Compliant with IP rights and visual copyright standards
Relevant AI technologies
Generative AIConversational AI / Chatbots