AI vs. Human Creativity in Fashion: Can AI Designers Truly Innovate?
The fashion industry has traditionally been driven by human creativity, intuition, and manual processes, deeply rooted in designers' artistic vision and traditional craftsmanship. However, with the advent of artificial intelligence (AI), the industry is experiencing a profound transformation. As consumer demands evolve and competition intensifies, fashion brands increasingly integrate AI designers to stay competitive. According to a McKinsey survey, 73% of fashion executives see generative AI as a top priority for 2024, underscoring its potential. This blog explores how AI designers are changing fashion in real time, from predicting trends to generating unique designs and optimising supply chains.
The Role of AI Designers in Real-Time Fashion
AI designers help fashion brands design and automate production processes in real time, using advanced algorithms and machine learning to enhance every aspect of fashion design, from initial concepts to final products. By creating design variations quickly, predicting trends, and personalising products, AI designers allow brands to respond swiftly to market demands, reducing the time from concept to market and maintaining competitiveness.
Types of AI Solutions and Their Applications
The market for AI designers in fashion is diverse, with various AI solutions offering unique value propositions. Here are some of the key types of AI designers and what they bring to the table:
Photorealistic Design Generation AI
Photorealistic design generation AI transforms how fashion designers create and visualise their collections. This AI produces design sketches and photorealistic images, allowing designers to experiment with various styles, materials, and patterns without needing physical prototypes. These AI tools streamline the design process, reduce costs, and enhance creativity.
Some Examples of AI Designers Producing Photorealistic Design Sketches
Marvelous Designer 2024.1 by CLO Virtual Fashion introduces generative AI toolsets that allow users to create clothing patterns and printed designs from text prompts, enhancing the design process. It also includes features for generating PBR (Physically Based Rendering) texture maps, which help create realistic fabric textures and visualise garments with accurate material properties.
Resleeve AI assists users in creating AI-generated fashion designs and images by producing professional, print-ready results through 3D models of clothing items. It allows fashion brands to train the AI to reflect their unique brand identity and design heritage, ensuring consistency in style and quality.
Design Generation to Production AI
Design generation to production AI uses deep learning models trained on historical design data and current fashion trends to create new design concepts by combining different elements in innovative ways. These tools can generate a wide range of design variations, simulate fabric behaviour, and visualise designs in 3D. By enabling designers to explore more creative possibilities quickly and refine their collections efficiently, these AI systems enhance the creative process while saving time and resources.
Example of Design Generation to Production AI
Catalyst AI by Six Atomic utilises deep learning and computer vision to generate production-ready patterns. The AI analyses a brand's historical designs and patterns, ensuring new designs adhere to brand standards and specifications. Designers can interact with Catalyst AI via chat or by instantly uploading images and generating production-ready patterns, making it a versatile tool for generating and refining design concepts. This significantly speeds up the production process and enhances overall efficiency. Catalyst AI also integrates seamlessly with existing PLM systems, improving collaboration and ensuring a streamlined workflow from design to production.
Additional capabilities include:
Fabric Consumptions: Provides precise calculations of fabric requirements for each design.
Garment Specifications for All Sizes: Generates detailed garment specifications across different sizes, ensuring accurate production planning.
Bulk Marker: Creates optimised bulk markers for efficient fabric usage during cutting.
3D Simulation Files: Outputs 3D simulation files for virtual prototyping.
Virtual Try-On AI
Virtual try-on AI uses augmented reality and computer vision to show customers how clothes will look and fit on their bodies. These systems create virtual models based on user-provided measurements and images, overlaying clothing items onto these models to simulate the fit and appearance. This technology enhances the online shopping experience and reduces return rates.
Some Examples of Companies Working on Virtual Try-On AI
Google Virtual Try-On uses generative adversarial networks (GANs) to overlay clothing on real models with different body shapes and sizes, providing a realistic representation of fit and appearance.
Metail’s Ecohot, owned by Tronog now, utilises 3D body scanning and computer vision to create accurate 3D models based on user measurements, enabling virtual try-ons from multiple angles.
Supply Chain Optimisation AI
Supply chain optimisation AI forecasts demand and optimises inventory levels using predictive analytics. It analyses historical sales data, market trends, and other variables to predict future demand accurately. These AI systems use optimisation algorithms to manage inventory and streamline production processes, ensuring that the right amount of products is produced at the right time, promoting cost savings and sustainability.
Some Examples of Companies Working on Supply Chain Optimisation AI
Li & Fung implements machine learning algorithms and big data analytics to enhance supply chain operations. The AI analyses market trends and historical sales data to optimise inventory levels and reduce lead times.
Stylumia uses AI-powered demand forecasting and inventory optimisation tools that analyse sales data, market trends, and consumer behaviour and help brands manage inventory more efficiently.
Trend Prediction AI
Trend prediction AI is designed to forecast future fashion trends by collecting and analysing large datasets from multiple sources, such as social media, fashion shows, online searches, and retail sales. It uses machine learning algorithms to identify patterns and predict which styles, colours, and designs will become popular, helping brands stay ahead of the curve.
Some Examples of Companies Working on Trend Prediction AI:
Heuritech employs computer vision and machine learning to analyse social media images and identify emerging fashion trends. The AI translates these insights into actionable forecasts for brands.
Edited aggregates and analyses data from e-commerce websites using machine learning algorithms to predict trends, optimise assortments, and inform pricing strategies.
Final Thoughts on the Usage and Future of AI in Fashion
Integrating AI designers into fashion is innovating the industry by enhancing efficiency, creativity, and personalisation. AI technology streamlines the design-to-production process, optimises supply chains, and helps brands make smarter inventory decisions, promoting sustainability. As AI advances, its impact will grow, assisting brands to stay agile in a rapidly changing market.
However, the essence of fashion lies in human creativity. AI is a tool to enhance, not replace, human ingenuity. The collaboration between human creativity and AI can push innovation further, ensuring fashion remains a profoundly human art form enriched by technology.
