The professional design studio trusted by 1,500+ brands and creators. Powered by proprietary transformer-based architectures and latent diffusion models trained on 100,000+ footwear designs to generate production-ready 3D geometry from natural language and visual input.
TRUSTED BY LEADING BRANDS
Transformer-based multimodal encoders process natural language and visual input through cross-attention mechanisms. Trained on domain-specific corpus of 100,000+ annotated footwear designs.
Denoising diffusion probabilistic models operate in compressed latent space for efficient high-resolution generation. Custom conditioning architecture maintains manufacturing constraints during sampling.
Signed distance functions (SDF) and occupancy networks generate watertight 3D meshes. Differentiable rendering enables end-to-end training on 2D supervision with 3D geometric priors.
Mixture-of-experts architecture routes inputs through specialized sub-networks. Selective ensemble combines multiple model predictions weighted by confidence scoring and manufacturing feasibility metrics.
Few-shot learning and meta-learning protocols enable rapid fine-tuning on client design libraries. Style embeddings learned through contrastive objectives capture brand-specific aesthetic signatures.
Computer vision pipeline extracts dimensional parameters, material properties, and construction details. Outputs conform to PLM/PDM data schemas for downstream manufacturing systems.
"We were spending 3-4 weeks per design iteration with traditional CAD. ShoeLabs lets us generate 20 variations in an afternoon, then refine the best ones. The mesh topology is actually production-ready—our factory accepted the files without modification."
"I uploaded sketches from my notebook, got back fully textured 3D models in under a minute each. Exported to Blender, made minor tweaks, and had 15 designs ready for my portfolio that same day. Landed my first brand partnership two weeks later."
"The brand DNA training is the real differentiator. After fine-tuning on 80 of our past designs, every output maintains our signature aesthetic—the chunky sole profile, specific panel proportions, even the eyelet placement patterns. It's like having a junior designer who actually understands our brand language."
Multimodal encoders extract semantic features from text and visual inputs. Attention mechanisms identify design intent, structural requirements, and aesthetic constraints.
Diffusion models sample from learned distribution conditioned on encoded features. Iterative denoising process generates high-fidelity representations in compressed latent space.
Neural implicit functions decode latent vectors into 3D geometry. Marching cubes extracts polygonal mesh with topology optimization for manufacturing constraints.
Production ML system built on distributed GPU infrastructure with continuous training pipelines.
Diffusion backbone with 2.3B parameters. Geometry network at 800M parameters. Style encoder at 150M parameters. Training compute across 512 A100 GPUs.
100,000+ annotated design images. 50,000+ production-grade 3D models. 10,000+ technical specification documents. Dataset spans 75 years of footwear design history.
Average generation time under 60 seconds. 94% of outputs are manufacturing-feasible. System handles 10,000+ API requests daily. 99.9% uptime SLA maintained.
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