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![Motion graphics created by using Seedance 2.0 on Nsketch AI Prompt ⤵️ Ultra-sleek motion design sequence. Pure black background throughout. White-on-black minimal aesthetic. No cuts — one single continuous unbroken visual evolution. Locked-off static center frame, no camera movement. All motion occurs within the subject. Self-luminous white forms on absolute black, no grain, no shadows. 0–1s [ORIGIN]: A single white point materializes at center frame. It pulses once — three concentric rings ripple outward like a heartbeat and dissolve into black. 1–2s [LINE]: The point stretches into a razor-thin white horizontal line extending edge to edge with elastic ease-out motion. 2–4s [GRID → CIRCLE]: The line multiplies into a precise grid of nine parallel horizontal and vertical lines appearing in staggered succession. The grid warps — outer lines curve inward, the structure rotates, straight lines bend into arcs, converging into a perfect geometric circle outline. 4–6s [WIREFRAME → SOLID SPHERE]: The circle extrudes into a 3D wireframe sphere, every latitude and longitude line crisp, slowly rotating on its vertical axis. The wireframe fills in smoothly — dissolving into a solid white surface with a soft specular highlight in the upper-left quadrant. 6–8s [INTERFACE]: The sphere flattens and unfolds into a clean rectangular plane — a minimal phone interface with rounded corners. Abstract UI elements appear inside: status bar, a grid of rounded app icons, home indicator bar. White line art on black. 8–10s [CUBE → BLOOM]: The interface folds inward, collapsing into an isometric wireframe cube that tumbles once. The cube's six faces peel outward simultaneously, each becoming a smooth white elliptical petal — blooming open like a flower in zero gravity. 10–12s [VORTEX]: The flower dissolves into hundreds of tiny white particles orbiting the center in a controlled elliptical vortex, some clockwise, some counterclockwise, each following a precise mathematical path, flickering softly. 12–13s [WATCH]: The particles decelerate and self-organize — converging and snapping into position to assemble the silhouette of a wristwatch: a clean rectangular case outline with lugs, built entirely from particles. 13–14s [COLLAPSE]: The watch dissolves into fluid white ribbons that compress into a single dense white sphere. It pulses once — brighter than before — then collapses rapidly inward to a single brilliant point. 14–15s [LOGO]: The point holds for one silent beat. Then it expands into a clean minimal brand logo — white sans-serif letterforms on black. A single pulse of warm amber light blooms from center behind the logo, fades to black. White text remains. Style: luxury tech brand motion identity, Bauhaus-minimalist, mathematical precision. Mood: calm, inevitable, intelligent, premium.](https://image.24vids.com/tw-2041392542659584302/ext_tw_video_thumb/2041392519330840576/pu/img/gJnakn0aUsx_Pqnd.jpg)
![Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!](https://image.24vids.com/tw-2069277649655243140/amplify_video_thumb/2069276621698465792/img/7_y1PzRYfzBgLDJ6.jpg)