Deep Capture

Nike Deep Capture Studio · 3D Visualization Specialist / Lead Photographer · Applied AI Creative Technologist · 2023–2026

The brief. Nike’s Deep Capture Studio needed two things: a photogrammetry capture system that could deliver production-grade product scans on a studio schedule, and a way to move generative AI from experiments into that production line.

My own short, not a Nike production. The rig is real; the lab is not. No studio footage appears on this site.

▶ Cinematic short · 0:49

THE RIG IS REAL. THE LAB IS NOT.

LinkedIn cut, 0:49. Higgsfield, Premiere. Stills generated first, then animated. Cut in Premiere with music and effects on separate tracks. No studio footage appears anywhere on this site.

What I did. I specified and built the photographic side of a six-camera capture system: camera bodies, optics, lighting, and a custom exoskeleton sourced from Esper in the UK, with a $100,000 build budget. A 3D-artist colleague owned the downstream mesh processing; my job was to make sure every pixel going into that pipeline was as clean and repeatable as a product shoot. Roughly 1,000 to 1,500 captures per SKU, 100 to 125 SKUs a season.

Then I led the studio’s generative AI adoption: benchmarked Google’s Gemini and Veo APIs against Vizcom, built the production pipeline (batch processing, templating, repeatable render setup, product-detail adherence), and trained three teammates on it. Our team of five delivered 100+ 3D product visualizations with a specialized X-ray render treatment in four days, against an outside vendor bid of $125,000 to $150,000 and a three-to-four-week timeline.

Stack. Sony cameras · Esper capture rig, lighting, and capture software · RealityCapture · Nuke · Substance Painter.

What it shows. Hardware, optics, and lighting judgment applied to a generative pipeline, then the pipeline itself: evaluation, build, training, delivery. The capture side is where most AI-in-production projects fail; this one didn’t.

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