wifi-densepose/aether-arena/calibration

RuView Calibration Service (reference implementation)

Turn a shared WiFi-CSI pose base model into a room-specific one with a 30-second labeled calibration and a ~11 KB per-room LoRA adapter. This is the deployable resolution of the cross-subject / cross-environment generalization problem (full study: ADR-150 §3.3–3.6).

Why

Zero-shot WiFi pose generalizes poorly to a new room or new person — an unseen room can drop a strong model to near-random. But that gap is not algorithmically closeable (CORAL, DANN, instance-norm, contrastive foundation-pretraining all failed) and not closeable by collecting more subjects (saturates ~64%). It is closeable, cheaply, at deployment time: a handful of labeled frames from the actual room pin down its multipath instantly.

Deployment case	Zero-shot	+ in-room calibration
Same room, new person (cross-subject)	64%	76% (200 samples)
New room + new person (cross-environment)	~10%	60% @ 5 samples → 73% @ 200

Verified demo (this code, source-only base on an unseen MM-Fi room E04): zero-shot 3.09% → after 200-sample calibration 74.29% (+71 pts).

How it works

A frozen shared base (transformer + temporal attention pool + skeleton-graph head, the published ruvnet/wifi-densepose-mmfi-pose) plus a tiny LoRA adapter (rank 8 on the input projection + pose head — 11,200 params ≈ 11 KB int8 / 22 KB fp16) fitted per room. Thousands of room-adapters hang off one base.

Usage

# 1) Capture a short labeled clip in the deployment room -> calib.npz {X:[N,3,114,10], Y:[N,17,2]}
#    (~100–200 samples recommended; below ~20 the adapter can underperform zero-shot)

# 2) Fit the per-room adapter (~11 KB):
python calibrate.py --base pose_mmfi_best.pt --data calib.npz --out room.adapter.npz

# 3) Run calibrated inference (base + room adapter):
python infer.py --base pose_mmfi_best.pt --adapter room.adapter.npz --data frames.npz --out kp.npy
#    omit --adapter to run the uncalibrated (zero-shot) base

X is CSI amplitude [N, 3 antennas, 114 subcarriers, 10 frames] (per-sample standardization is applied internally). Y is [N,17,2] COCO keypoints in [0,1].

Calibration budget (measured, rank-8 LoRA, 3 seeds — ADR-150 §3.5)

Labeled samples/room	cross-subject	cross-environment
0 (zero-shot)	64%	~10%
5	—	60%
20	66%	66%
50	70%	70%
200	72%	73%

Knee at ~50 samples (~70%); below ~20 samples the adapter can hurt (too few to fit reliably).

Two models, two producers (not interchangeable)

Adapters are model-specific. There are two calibration producers here:

Producer	Target model	Input	Adapter format	Consumer
calibrate.py	MM-Fi transformer (pose_mmfi_best.pt, 3×114×10)	[N,3,114,10]	.npz (proj/head LoRA)	this Python infer.py
cog_calibrate.py	cog conv+MLP (pose_v1.safetensors, 56×20)	[N,56,20]	.safetensors (fc1.a/fc1.b/fc2.a/fc2.b)	Rust cog-pose-estimation run --adapter

# Produce a cog-format per-room adapter for the deployed Rust pose engine:
python cog_calibrate.py --base pose_v1.safetensors --data calib.npz --out room.safetensors
# then in the cog runtime:
cog-pose-estimation run --config <cfg> --adapter room.safetensors

Same LoRA mechanism (ADR-150 §3.5), different architecture and key layout — an adapter from one producer will not load into the other model.

Notes

• Calibration only helps when the base hasn't already seen the room. The published flagship was trained on MM-Fi random_split, so calibrating it on an MM-Fi subject is a near-no-op (it already saw them); for a genuinely new real-world room it is zero-shot and calibration applies. To reproduce the demo on a held-out MM-Fi room, train a source-only base (exclude the target environment) — see ADR-150 §3.6 and the few-shot harness in aether-arena/staging/.
• Adapter is saved fp16 (~22 KB); quantize to int8 for the ~11 KB on-device form.
• Inference is real-time on CPU (the 75 K-param micro variant runs in 0.135 ms single-thread x86; see docs/benchmarks/wifi-pose-efficiency-frontier.md).