Blog
Series The origin story of META FIT: how a decades-old vision of seeing yourself in clothes before buying evolved from hardware kiosks to GAN-powered virtual try-on, plus a comprehensive survey of 15+ VTON research models.
Series A code-level walkthrough of the Parser-Free Appearance Flow Network: Feature Pyramid encoding, CUDA-accelerated correlation kernels, optical flow warping, and the ResUnet generator that composites garments onto people.
Series A deep dive into Generative Adversarial Networks: how the generator-discriminator dynamic works, why GANs dominated image generation before diffusion models, and how they power virtual try-on systems.
Series How OpenPose skeletal detection, Graphonomy human parsing, and custom body measurement algorithms work together to enable accurate virtual fitting — plus an exploration of PiFu for 2D-to-3D reconstruction.
Series What the GAN-based virtual try-on system achieved, where it failed (and why), the smartphone app design, and how diffusion models are changing everything for the next generation of META FIT.
Series How I tackled the universal 'what's for dinner' problem over a decade ago using classical data science — cleansing 20,000 recipes, 200,000 ingredient records, and nutritional data into a unified ML-ready dataset.
Series Using cosine similarity on nutritional vectors to find recipes that match a target meal's nutrition profile but offer completely different flavors — at both the recipe and menu level.
Series Years after building the original ML pipeline, LLMs changed everything — using the ChatGPT API to simplify elaborate recipes into weeknight-friendly meals, and reflecting on a decade-long journey from cosine similarity to LSTM to GPT.
Series Reframing meal planning as a text generation problem — using LSTM neural networks with temperature-controlled sampling to predict diverse, non-repetitive menus from historical meal sequences.