Magic Mirror
Magic Mirror
Project brief
ExecuTorch Hackathon — Founders, Inc., October 17–18, 2026. Everything below runs on the Arduino VENTUNO Q on the desk: no cloud, no account, no network needed once the board is flashed.
The pitch
A store fixture that puts the shopper in the product photo. Stand in front of a camera and you appear wearing the garment — the head cutout, the body matte, the garment placement and the generated try-on image are all computed on the board's Hexagon NPU, with the web page that shows the result served from the same device.
What's built and measured
Piece Where it runs Cost
Virtual billboard (BiSeNet head matte → product photo) NPU 12 ms compute, ~1 fps end to end
Person segmentation (SegFormer human parsing) NPU 24.9 ms
Generated try-on (Stable Diffusion + ControlNet-Canny) NPU ~0.3 s per step
Face swap into a studio shot (GHOST, Apache-2.0) NPU 82 ms, fp16
LLM text generation (Gemma 4 E2B) NPU 188 tok/s prompt, 16.3 tok/s gen, 494 MiB
Vision Q&A (InternVL3-1B) NPU 0.5 s encode, ~33 tok/s
Live speech (Whisper-tiny → Gemma translate) NPU 1.8–2.6 s transcript, ~9 s translated
LLM baseline (Llama 3.2 1B, XNNPACK) CPU 10.1 tok/s
The NPU numbers are the point: prompt eval is ~12× the CPU baseline on a model twice the size, with resident memory a fraction of it, because the weights live in the NPU context.
How it fits together
Hugging Face model → optimum-cli export executorch → quantized .pte → ExecuTorch runtime. The same .pte runs on the Mac and on the board, which is what makes iterating on a laptop and demoing on hardware the same workflow. NPU models go through the track bundle's QNN AOT path (Linux only) and are driven by a warm persistent runner — 468 ms per inference launched cold, 26 ms held open with scratch on tmpfs.
Two independent routes to the same effect, kept separate on purpose: overlay moves a garment onto a live frame (garment_overlay.py, garment_clip.py), and face swap moves the shopper's face into a studio shot that already has the garment, lighting and pose (board_faceswap_*).
Hardware
QCS8275, Cortex-A78C big cores, 14 GB RAM, Ubuntu 24.04 aarch64 over adb — not Android, despite answering adb, and considerably better than its published spec. No root; the compiler, the runners and the Python environment were all installed without it.
Status and limits
Board path: working, demoed, including the web UI and the walkthrough deck.
Android/XR app (android/): compiles, never run — no device was attached.
macOS cannot do QNN ahead-of-time compilation, so NPU work happens on the board.
Thin segmentation classes (arms) lose accuracy at 8a8w; silhouettes don't.
The board reboots under heavy load, so long board-side jobs are resumable.
Harshith Ande's team
Kamikaze
Kamikaze is an edge-AI vision system built on the Arduino VENTUNO Q that converts physical consumer items into interactive 3D digital twins in real time. Powered by an on-device Qualcomm Hexagon NPU, the pipeline uses YOLO-World, DINOv2, RapidOCR, and HSV color gating to identify smartphones and beverage cans during a guided five-second scan. An integrated FastMCP server then resolves metric-accurate 3D models by cross-referencing local CAD caches, 1:1 dummy repositories, and procedural mesh generators with live-stitched label textures—all while enforcing a zero-cloud privacy boundary. The resulting Smart Kiosk interface pairs Modulino RGB LED feedback and real-time hardware telemetry with an auto-orienting 3D viewport that dynamically mirrors the physical pose of the object in the user's hand.
Qualcomm
