Outfit recommendation
A daily outfit based on weather, event and style context.
Project overview
A mobile style assistant that learns the user’s wardrobe, recommends outfits based on weather and calendar context, and offers virtual try-on.
Challenge
Outfit recommendations had to be shaped not only by product similarity but by weather, event type and personal style. The virtual try-on output had to look convincing.
Our approach
We built a multimodal recommendation architecture where visual embeddings, context signals and user feedback combine into a single score. For try-on we developed a diffusion pipeline that preserves pose and fabric drape, and cut latency with on-device pre-processing.
- 0%
- Recommendation acceptance
- < 0s
- Try-on time
- 0.0
- Store rating
- 0%
- Weekly active users
Features
The main systems we built for this project
Virtual try-on
Realistic garment fitting on the user’s own photo.
Match score
A percentage outfit score based on colour harmony, cut and context.
Digital wardrobe
Cataloguing owned items and suggesting missing pieces.
Explainable recommendations
Transparent suggestions with “why this outfit?” reasoning.
Gallery
Screen designs
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