Realistic Product Rendering using DreamBooth & Hugging Face

This project demonstrates the use of generative AI for producing high-quality, human-like product visuals using brand-specific assets. It enhances product presentation in marketing without the need for physical photoshoots.

Problem Statement

Manual product photoshoots for marketing are expensive and time-consuming. The aim was to generate realistic images of helmets and jackets being worn, using only sample product visuals and AI.

Results

Prompt engineering was essential to ensure contextually relevant generation. I used prompts involving age groups, poses, lighting, and attire settings. The system was deployed with a Gradio-based interface for iterative generation. The outputs were tested with the marketing team, and results showed that asset generation time dropped by over 80%, with image quality rated on par with professional photoshoots.

Process

As part of a marketing-focused internship, I developed a generative AI pipeline that allowed realistic visualization of helmets and jackets on humans using AI-generated imagery. I began with sample product images from the company's website and performed custom fine-tuning of a Stable Diffusion model using DreamBooth. Using Hugging Face Diffusers, I conditioned the model to generate on-brand, photorealistic results.

Conclusion

This project shows how generative AI can deliver real business value. It streamlined content production, empowered marketing teams with flexible asset generation, and offered a scalable alternative to traditional workflows.