Digital Asset Challenges in Fashion E-commerce

Visual content is an important part of ecommerce, where compelling product imagery is essential for customer engagement and conversion.
E-commerce fashion companies face a complex array of challenges in digital asset production, including the high cost of traditional studio photography.
A single SKU can incur expenses exceeding hundreds of euro, covering fees for stylists, photographers, models, studio rentals, and extensive post-production editing. For entire large catalogs, budgets can easily escalate into large figures, as research indicates that the cost per image can reach 130 EUR in some cases.
Beyond budget consideration traditional image production methods can mean slow time-to-market and operational bottlenecks. Traditional photoshoots are inherently rigid and time-consuming, often requiring weeks for production and post-production. This creates significant delays in launching new collections, hindering a brand’s ability to respond swiftly to dynamic fashion trends and market shifts. 

“Lay-flat to model” AI workflows tailored to the e-commerce fashion industry

Octagram is experimenting with custom “lay-flat to model” AI workflows tailored to the fashion industry.
We think the future of digital asset production for e-commerce will be “hybrid” —mixing studio photography of real products with photography of fashion models (human or virtual) and at some point might become fully digitalised, when real products will be replaced by digital twins.
Octagram’s working on refining a “lay-flat to model” process based on a custom AI workflow, aimed at reducing costs and the time to market for the ecommerce fashion industry. This process involves digitally styling a fashion model with real apparel items photographed in a flat lay arrangement within a studio setting.

Image illustrating the lay-flat to model image input and results.
Image illustrating the lay-flat to model image input and results.
Image illustrating the lay-flat to model image input and results.

Current limitations of the “lay-flat to model” workflow

While the “lay-flat to model” workflow demonstrates potential, it is currently in an experimental testing phase.
A key challenge is achieving absolute photorealism, especially for intricate details/patterns and for capturing fine-grained elements like fabric texture, stitching, subtle wrinkles, and the nuances of shiny materials.

Furthermore, ensuring visual consistency across multiple product variations (e.g., the same garment in different colors or sizes) is an ongoing area of development. Current AI-generated models may sometimes lack the subtle personality or dynamic poses of human models, leading to a “dry” aesthetic. Varying shot types, close-ups, and maintaining character consistency across different visuals are still areas where human oversight is often required.

Despite advancements, the quality control for AI-generated images is not yet fully automated. Developing robust automated quality assurance systems that can reliably evaluate image realism, relevance, and consistency, and flag subpar outputs for human review, is crucial for widespread operationalisation. This highlights the continued need for human expertise in the loop. The rapid rise of AI-generated content also introduces significant ethical and legal complexities. Concerns include data privacy, security, the potential for misuse (e.g., deepfakes), and, critically, intellectual property (IP) and licensing rights. Brands must ensure they secure long-term, royalty-free rights to use and repurpose AI-generated visuals, especially for commercial applications.

What the future holds

However, these challenges are actively being addressed by an accelerating pace of innovation in generative AI. AI algorithms are becoming increasingly sophisticated, and the quality of photorealistic content is continuously enhancing. A major focus of ongoing research is on improving model capabilities to generate higher-resolution, more photorealistic images with intricate detail. The industry is actively developing solutions for achieving greater consistency across product variations and establishing more robust automated quality assurance systems to streamline workflows further.

e-commerce assets experiment generative AI lay-flat-to-model media production