Multimodal Generative AI (Image/Text-to-3D)
Generative AI pipelines converting images or text into consistent 3D representations.
Overview
Built end-to-end generative AI systems that create 3D models from 2D images or textual descriptions. The system leverages multimodal foundation models fine-tuned for geometric consistency and visual quality.
Challenge
Generating 3D content from 2D inputs is inherently ill-posed. Ensuring geometric consistency, texture quality, and semantic alignment between input modalities requires sophisticated AI model architectures and training strategies.
Solution
Fine-tuned multimodal foundation models with custom loss functions emphasizing geometric plausibility. Implemented a two-stage approach: coarse geometry generation followed by detail refinement. Used synthetic data to improve generalization.
Impact
Reduced 3D content creation time from hours to minutes. System enables rapid prototyping for design and enables non-experts to create 3D content through natural language descriptions.
Key Highlights
- Fine-tuned multimodal foundation models for 3D generation
- Controlled geometry, texture quality, and semantic alignment
- 10x faster than manual 3D modeling for common objects