AI strategies for mass image processing in eCommerce: An overview and practical insights at Doopic

Introduction

In recent months, artificial intelligence (AI) has made huge strides in image processing, especially for eCommerce, offering a wide range of applications for handling large volumes of images. AI enables companies to automate, scale, and improve their image workflows, delivering high-quality visuals faster and more cost-effectively. Generative AI models, in particular, show promise in potentially replacing expensive model photoshoots or location productions, while also pushing boundaries beyond traditional imagery. In this post, we’ll provide an overview of these AI advancements and explore the strategies we’re using at Doopic for profitable implementation.

Current AI Applications in eCommerce Image Processing

AI's capabilities depend on available models and how consistently they deliver results based on standardized prompts. These models fall into three main categories, each with unique strengths:

1. Manipulative AI: Enhances existing images by cropping, retouching, compressing, or adjusting colors.
2. Analytical AI: Identifies and categorizes image content, such as patterns, textures, faces, and body parts.
3. Generative AI: Creates entirely new images based on prompts, like virtual models or custom product images.

At Doopic, we test and apply these AI approaches in three ways:

1. Optimizing Traditional Image Processing: We use AI to automate tasks like object removal and retouching, reducing time and costs while improving consistency.
2. Simulating Studio Photoshoots with Generative AI: By blending real product images with AI-generated models (e.g., for hollowman or model-detail shots), we reduce the need for expensive photoshoots, models, and travel.
3. Experimental AI for Creative Imagery: We explore AI’s limitless potential for creative visual content, especially for social media, where traditional product photography norms are less important.

Challenges in AI Adoption

‍Despite AI’s rapid development, several hurdles remain, including:

1. Model “Immaturity”: Current AI models can struggle with accuracy, producing unrealistic or inconsistent results, especially with fine details like colors, fonts, and textures. This is particularly challenging when merging real and AI-generated images, where discrepancies in resolution and design can emerge.
2. Constant Innovation: AI technology evolves quickly, often making existing solutions obsolete within days. This demands a flexible approach, with regular testing of new models and adaptable workflows that can seamlessly integrate better tools as they become available.

Conclusion

The integration of AI into mass image processing holds vast potential, especially for eCommerce. However, current AI models are still developing, and achieving reliable, high-quality results remains a challenge. At Doopic, we are tackling these hurdles with a flexible strategy that combines AI-driven automation with rigorous correction processes, enabling us to stay ahead of the curve while meeting the growing demands of the industry. The future lies in refining these workflows for greater efficiency and pushing the boundaries of what’s possible in product photography.