AI for Fashion eCommerce: Product Tagging & On-Model Imaging
Budget: $5,000 – $10,000 USD
Project Overview
We are looking for an expert AI/Computer Vision developer (or small team) to build a scalable, production-ready AI pipeline for a fashion eCommerce catalog. The goal is to automate product tagging and generate photorealistic on-model images from flat-lay photos.
We prioritize cost-effective solutions leveraging state-of-the-art (SOTA) open-source models (e.g., IDM-VTON, OOTDiff, Stable Diffusion XL).
1. Automated AI Product Tagging
• Volume: 20,000–40,000 SKUs/year (Batch processing required).
• Scope: Fashion only (Apparel, Accessories, Footwear).
• Task: Build a CV-based engine to extract attributes (Category, Color, Material, Fit, Pattern, Style, etc.).
• Taxonomy: Consultant should help define a scalable tagging structure.
• Requirement: High accuracy (Target 95%+) and consistent metadata output (JSON/CSV).
• Note: No internal labeled dataset available. Developer must handle data curation or use pre-trained fashion models.
2. AI On-Model Generation (Virtual Try-On)
• Input: Flat-lay or ghost mannequin images.
• Output: Photorealistic on-model images (~5 poses per SKU).
• Virtual Model Strategy: * Establish 5 base digital human models (2 Male, 3 Female) with fixed identities.
• Maintain consistent lighting, shadows, and garment textures.
• Technology: Open to using diffusion-based models and VTON pipelines for efficiency.
Technical Requirements & Expectations
• Proven Experience: Portfolio in fashion CV or Virtual Try-on is a MUST.
• Approach: Strong preference for leveraging and fine-tuning open-source models rather than building from scratch.
• Infrastructure: Scalable batch processing via API (AWS/GCP/RunPod).
• Deployment: Production-grade stability and fast inference time.
• Payment Plan (Milestone-based):
1. Phase 1: Taxonomy Design & Prototype for Tagging Engine.
2. Phase 2: Full Tagging Pipeline Deployment.
3. Phase 3: Development of 5 Base AI Models & Image Gen Pipeline.
4. Phase 4: Final Integration & Scaling/Optimization.
Final payment is contingent upon meeting the agreed-upon KPIs: minimum 95% accuracy for tagging and commercial-grade realism for AI-generated images. A performance validation phase will be required before the final milestone release
How to Apply:
1. Share your portfolio of similar fashion AI projects (Tagging or VTON).
2. Technical Assessment Question: > "Have you ever built a system similar to Vue.ai or Lalaland.ai? If so, please briefly describe the high-level API architecture and the data flow between the image processing engine and the generation model. (Answers like 'I can do it' without technical detail will be ignored.)"
3. Which open-source models (e.g., SDXL, IDM-VTON, Florence-2) do you plan to use for this project?
4. Provide a rough estimate for the cost per additional digital model.
Reference Platforms (Inspiration)
• https://vue.ai
https://www.vue.ai/products/on-model-imagery/
https://vue.ai/solutions/automated-product-tagging/
• https://www.lalaland.ai
We are looking for teams who can build better proprietary systems.
We aim to combine Vue.ai's advanced tagging logic with Lalaland.ai's high-fidelity visual quality into a single seamless pipeline
Payment will be released strictly by milestones based on performance (accuracy & realism)
We will review your technical answer to the question above. Shortlisted candidates will be invited for a brief technical interview and a paid test task (generating a sample on-model image).
We are looking for an expert AI/Computer Vision developer (or small team) to build a scalable, production-ready AI pipeline for a fashion eCommerce catalog. The goal is to automate product tagging and generate photorealistic on-model images from flat-lay photos.
We prioritize cost-effective solutions leveraging state-of-the-art (SOTA) open-source models (e.g., IDM-VTON, OOTDiff, Stable Diffusion XL).
1. Automated AI Product Tagging
• Volume: 20,000–40,000 SKUs/year (Batch processing required).
• Scope: Fashion only (Apparel, Accessories, Footwear).
• Task: Build a CV-based engine to extract attributes (Category, Color, Material, Fit, Pattern, Style, etc.).
• Taxonomy: Consultant should help define a scalable tagging structure.
• Requirement: High accuracy (Target 95%+) and consistent metadata output (JSON/CSV).
• Note: No internal labeled dataset available. Developer must handle data curation or use pre-trained fashion models.
2. AI On-Model Generation (Virtual Try-On)
• Input: Flat-lay or ghost mannequin images.
• Output: Photorealistic on-model images (~5 poses per SKU).
• Virtual Model Strategy: * Establish 5 base digital human models (2 Male, 3 Female) with fixed identities.
• Maintain consistent lighting, shadows, and garment textures.
• Technology: Open to using diffusion-based models and VTON pipelines for efficiency.
Technical Requirements & Expectations
• Proven Experience: Portfolio in fashion CV or Virtual Try-on is a MUST.
• Approach: Strong preference for leveraging and fine-tuning open-source models rather than building from scratch.
• Infrastructure: Scalable batch processing via API (AWS/GCP/RunPod).
• Deployment: Production-grade stability and fast inference time.
• Payment Plan (Milestone-based):
1. Phase 1: Taxonomy Design & Prototype for Tagging Engine.
2. Phase 2: Full Tagging Pipeline Deployment.
3. Phase 3: Development of 5 Base AI Models & Image Gen Pipeline.
4. Phase 4: Final Integration & Scaling/Optimization.
Final payment is contingent upon meeting the agreed-upon KPIs: minimum 95% accuracy for tagging and commercial-grade realism for AI-generated images. A performance validation phase will be required before the final milestone release
How to Apply:
1. Share your portfolio of similar fashion AI projects (Tagging or VTON).
2. Technical Assessment Question: > "Have you ever built a system similar to Vue.ai or Lalaland.ai? If so, please briefly describe the high-level API architecture and the data flow between the image processing engine and the generation model. (Answers like 'I can do it' without technical detail will be ignored.)"
3. Which open-source models (e.g., SDXL, IDM-VTON, Florence-2) do you plan to use for this project?
4. Provide a rough estimate for the cost per additional digital model.
Reference Platforms (Inspiration)
• https://vue.ai
https://www.vue.ai/products/on-model-imagery/
https://vue.ai/solutions/automated-product-tagging/
• https://www.lalaland.ai
We are looking for teams who can build better proprietary systems.
We aim to combine Vue.ai's advanced tagging logic with Lalaland.ai's high-fidelity visual quality into a single seamless pipeline
Payment will be released strictly by milestones based on performance (accuracy & realism)
We will review your technical answer to the question above. Shortlisted candidates will be invited for a brief technical interview and a paid test task (generating a sample on-model image).