Proposed: Automated Clothing Pipeline

Context

This proposal outlines an integrated system for clothing detection, recommendation, generation, and quality assurance. Key metrics include real-time processing latency (<50ms), recommendation accuracy (>85% user satisfaction), and defect detection recall (>90%). The system addresses growing demands in virtual fashion markets by combining computer vision, recommendation systems, and procedural content generation.

Problem Statement

Key challenges include:

High computational cost of real-time clothing detection on edge devices
Cold-start problem for personalized recommendations
Manual effort in 3D clothing asset creation
Difficulty detecting rare defects in skinned meshes

Proposed Solution

Four interconnected modules using unified metadata: Implementation Steps:

Task A: Optimize RF-DETR with IREE compiler for edge deployment
Task B: Build PinSage-based recommender with multi-tag support
Task C: Develop Blender/Marvelous Designer automation pipeline
Task D: Train defect detection model with synthetic data

Implementation Plan

Phase 1: Core System Development (Weeks 1-6)
- Validate IREE compatibility for deformable attention
- Implement split_multi_value in librecommender
- Create Blender defect injection scripts
- [Roboflow] Create clothing classification dataset with style/color annotations
Phase 2: Integration & Optimization (Weeks 7-10)
- Deploy edge-optimized RF-DETR on Jetson AGX
- Establish Godot/Unreal asset pipeline
- Benchmark defect detection F1 scores
- [Roboflow] Generate synthetic defect dataset using Blender Python API
Validation:
- Success Criteria: <20ms inference latency, >85% recommendation accuracy
- Failure Threshold: >30% performance drop vs baseline

Benefits

10x faster clothing asset creation vs manual workflows
Unified metadata flow from detection to 3D generation
Real-time operation on consumer-grade hardware
Automated QA for production-ready 3D models

Risks and Limitations

IREE compilation failures (Mitigation: Maintain PyTorch fallback implementation)
Physics simulation instability (Mitigation: Implement Marvelous Designer safety constraints)
Rare defect false positives (Mitigation: Active learning with human-in-the-loop)

Alternatives Considered

Option	Pros	Cons
YOLOv8	Faster inference	Lower mAP on small accessories
TensorFlow Recommenders	Mature ecosystem	No native multi-tag support

When to Avoid This Solution

Not suitable for high-fashion detail reproduction requiring manual artistry.

Organizational Alignment

Supports virtual commerce initiatives and aligns with open-source 3D tooling roadmap.