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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