Page 506 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

3. Small-scale retailers may face difficulties integrating AI solutions.

This AI-powered system revolutionizes Indian fashion retail by bridging traditional and modern
fashion, enhancing user experience, and promoting sustainability.

2�2 Benefits of the use case

1. Addresses key sustainability challenges in India’s modern and traditional fashion industry.
India's fashion sector, with its mix of handcrafted textiles and mass-produced apparel,
struggles with high return rates, overproduction, and excess textile waste. Virtual try-on
technology powered by generative AI models (such as Stable Diffusion for hyper-realistic
visualizations and Transformer-based AI for personalized garment design) can significantly
reduce waste by helping consumers make more informed purchase decisions. AI-driven
demand forecasting helps brands better anticipate consumer preferences and produce
accordingly, reducing overstock and textile waste.
2. AI-powered garment generation helps revive traditional weaving techniques by enabling
digital customization of handcrafted designs. By digitizing and preserving cultural
craftsmanship, the proposal enhances economic opportunities for artisans and helps
designers make the right garment design decision,
3. Climate action benefits from AI’s ability to reduce return-related logistics emissions and
minimize excess textile production, both of which contribute to carbon emissions. AIs
predictive capabilities allow brands to manufacture only what is needed, cutting down
industrial waste and energy usage.

2�3 Future Work

1. Data collection: Curating a high-quality, diverse dataset is foundational for robust model
performance. This includes collecting images of different types and styles of garments,
traditional textile patterns (e.g., Banarasi, Kanjeevaram, Chikankari, Baluchari, Chanderi),
user poses across body types, backgrounds, and consumer preference data. Attention
must be paid to ensuring demographic and cultural representation, and to ethical data
practices including consent and anonymization.
2. Proof of concept development: A functional PoC will demonstrate key capabilities such as
virtual try-on accuracy, garment texture preservation, and generative design from text or
sketches. This phase will validate the technical feasibility, user experience, and business
impact, using limited-scale deployments via mobile apps or kiosks at artisan fairs or retail
pilots.
3. Model development: Core components include: (1) generative models for garment
synthesis, (2) embedding models for texture consistency, (3) physics-aware simulation
modules for realistic draping, and (4) forecasting models for demand prediction.
This stage also involves fine-tuning models on region-specific data and optimizing
performance for real-time inference.
4. Create new variations/extensions to the same use case: The use case can evolve to support
applications like personalized garment recommendations, person avatar integration for
deeper realism, and virtual tailoring. Extensions could also focus on fashion education,
enabling students and artisans to design and preview garments digitally before physical
prototyping.
5. Standards development related to the use case: To ensure interoperability, fairness,
and scalability, the initiative must contribute to the development of technical standards
related to virtual try-on formats, AI-generated fashion metadata, and privacy-preserving
mechanisms in user-data-driven AI. Collaborating with industry and regulatory bodies will
be key to establishing the best practices for ethical and sustainable AI in fashion.

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