wifi-densepose/api-docs/adr/ADR-152-wifi-pose-sota-2026-intake.md

ADR-152: WiFi-Pose SOTA 2026 Intake — Geometry-Conditioned Calibration, External Benchmarks, and the Foundation-Encoder Training Recipe

Field	Value
Status	Proposed
Date	2026-06-10
Deciders	ruv
Codebase target	wifi-densepose-calibration (geometry conditioning, ADR-151 Stage 2), wifi-densepose-train (camera-supervised path, MAE recipe), wifi-densepose-cli (benchmark harness), docs
Relates to	ADR-151 (Per-Room Calibration), ADR-150 (RF Foundation Encoder), ADR-135 (Empty-Room Baseline), ADR-079 (Camera-Supervised Pose), ADR-027 (MERIDIAN), ADR-024 (AETHER), ADR-149 (AetherArena), ADR-029 (Multistatic)
Research provenance	Deep-research run 2026-06-10: 22 sources fetched, 110 claims extracted, 25 adversarially verified (3-vote), 24 confirmed / 1 refuted. Evidence grades per source below.

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1. Context

A structured survey of the 2025–2026 WiFi human-sensing state of the art was run on 2026-06-10 to answer: what should RuView integrate next, and does anything published invalidate our current direction? Every claim below was verified against the primary source by independent adversarial reviewers; evidence grades distinguish what the papers measured from what they merely claim. Almost all performance numbers are author-self-reported preprint results — treated here as CLAIMED until reproduced on our hardware.

1.1 The five verified findings

(F1) "Coordinate overfitting" is a named, diagnosed failure mode of camera-supervised WiFi pose — and our ADR-079 pipeline has the exact shape of it. PerceptAlign (arXiv 2601.12252, accepted ACM MobiCom 2026) shows that models regressing CSI directly to camera-frame coordinates memorize the deployment-specific transceiver layout; SOTA baselines degrade to >600 mm MPJPE in unseen scenes. Their fix is cheap: a <5-minute calibration using two checkerboards and a few photos to align WiFi and vision in one shared 3D frame, plus fusing transceiver-position embeddings with CSI features. Claimed: −12.3% in-domain error, −60%+ cross-domain error. They release the claimed-largest cross-domain 3D WiFi pose dataset (21 subjects, 5 scenes, 18 actions, 7 device layouts). Evidence: improvements CLAIMED (preprint w/ MobiCom acceptance); the failure mode itself is corroborated across the cross-domain literature — and independently by our own ADR-150 data (81.63% in-domain vs ~11.6% leakage-free cross-subject torso-PCK).

(F2) An external model named "WiFlow" claims 97.25% PCK@20 with 2.23M params and ships everything. arXiv 2602.08661 (Apr 2026) — spatio-temporal-decoupled CSI pose, 97.25% PCK@20 / 99.48% PCK@50 / 0.007 m MPJPE, 2.23M parameters (~2.2 MB int8). Code, pretrained weights, and a 360k-sample CSI-pose dataset are public under Apache-2.0 (repo, Kaggle dataset). Evidence: artifact availability MEASURED (verified by direct repo inspection); PCK numbers CLAIMED (5-subject, in-domain, self-collected dataset; hardware unspecified; 15 keypoints vs our 17). ⚠️ Name collision: this is unrelated to RuView's internal WiFlow model. In all RuView docs the external model is referred to as WiFlow-STD (DY2434).

(F3) For CSI foundation encoders, data scale — not model capacity — is the bottleneck, and the tokenization recipe is now known. UNSW's MAE pretraining study (arXiv 2511.18792, Nov 2025) — the largest heterogeneous CSI pretraining run to date (1,320,892 samples, 14 public datasets incl. MM-Fi, Widar 3.0, Person-in-WiFi 3D; 4 devices; 2.4/5/6 GHz; 20–160 MHz) — reports zero-shot cross-domain gains of 2.2–15.7% over supervised baselines, with unseen-domain performance scaling log-linearly with pretraining data, unsaturated at 1.3M samples, while ViT-Base adds only 0.4–0.9% over ViT-Small. Optimal recipe: 80% masking ratio, small (30,3) patches (+4.7% over (40,5) by preserving fine temporal dynamics). Evidence: MEASURED within-study (ablations verified in body text) but preprint; downstream tasks are classification, NOT pose — pose transfer is a hypothesis. Independently corroborates ADR-150's finding that capacity hurts cross-subject.

(F4) Hardware/standards: 802.11bf is finished; Espressif ships official sensing; Wi-Fi 6 AP CSI is reachable.

• IEEE 802.11bf-2025 published 2025-09-26 (verified against the IEEE SA record) — sensing standardization is complete for both sub-7 GHz and >45 GHz, with formal sensing setup/feedback procedures. No ESP32 silicon implements it yet. Evidence: MEASURED (standards-body record).
• Espressif esp_wifi_sensing (Apache-2.0, v0.1.x, ESP Component Registry): official CSI presence/motion FSM; esp-csi actively maintained (commit 2026-04-22, verified), CSI confirmed across ESP32/S2/C3/S3/C5/C6/C61. Evidence: MEASURED (vendor pages + commit log). ⚠️ A stronger "drop-in compatible with RuView nodes" claim was REFUTED 0-3 — WiFi-6 parts use a different CSI acquisition config struct.
• ZTECSITool (arXiv 2506.16957, code): CSI from commercial Wi-Fi 6 APs at up to 160 MHz / 512 subcarriers (~5–10× ESP32 subcarrier count; the gain is aperture, not per-Hz granularity). Firmware is gated behind a ZTE serial-number approval. Evidence: capability CLAIMED by the vendor-authored tool paper; code artifact MEASURED.

(F5) Nothing in 2025–2026 does full DensePose UV regression from commodity WiFi. Keypoint pose remains the field's frontier. Three "wireless foundation model" papers were screened out by full-text inspection (HeterCSI = simulated cellular channels only; the NeurIPS-2025 FMCW pilot = mmWave radar, presence-only; arXiv 2509.15258 = survey, no artifacts). Evidence: MEASURED (absence verified by full-text inspection of the candidates that surfaced; absence of evidence across the whole literature is necessarily weaker).

1.2 What this means for the ADR-151 calibration system

ADR-151's enrollment protocol captures guided human anchors but does not record or condition on transceiver geometry. F1 says that omission is precisely the thing that makes camera-supervised (and, plausibly, anchor-supervised) heads layout-brittle. ADR-151's per-room thesis ("teach the room before you teach the model") is strengthened by F1 — PerceptAlign is independent evidence that layout must be modeled explicitly — and the fix composes naturally with our Stage-2 enrollment.

ADR-150's masked-CSI-encoder design is validated by F3, which also hands us the hyperparameters and the priority call: collect/aggregate more heterogeneous CSI before scaling the encoder.

2. Decision

Adopt four changes, ordered by effort-vs-gain:

2.1 Geometry-condition the calibration system (extends ADR-151 Stage 2) — ACCEPTED

1. Record transceiver geometry at enrollment. EnrollmentProtocol gains an optional NodeGeometry record per node (position estimate, antenna orientation, inter-node distances where known). Stored alongside the room baseline in the bank; schema-versioned so existing banks remain readable.
2. Fuse geometry embeddings into specialist training. Where a specialist head consumes the (future, ADR-150) backbone embedding, concatenate a small learned embedding of NodeGeometry — the PerceptAlign mechanism, transplanted to our per-room banks. Statistical specialists (current) ignore it; LoRA heads (ADR-151 P6) consume it.
3. Adopt the two-checkerboard alignment for the camera-supervised path (ADR-079). When MediaPipe supervision is used, calibrate camera↔WiFi into one shared 3D frame before regression (<5 min, two checkerboards, a few photos). This is the direct defense against F1 for our 92.9%-PCK@20 pipeline.
4. Evaluate on the PerceptAlign cross-domain dataset (21 subjects / 7 layouts) as the MERIDIAN cross-layout benchmark — gated on confirming its license and downloadability (open question; repo per paper: github.com/Trymore-lab/PerceptAlign).

2.2 Benchmark against WiFlow-STD (DY2434) — ACCEPTED

Pull the Apache-2.0 weights + 360k-sample dataset; run three measurements: (a) their model on their data (reproduce 97.25% claim), (b) their model fine-tuned on our ESP32 17-keypoint eval set, (c) our internal WiFlow on their dataset (15-keypoint subset mapping). Until (a)–(c) are measured, no RuView doc may cite 97.25% as a comparable number — different dataset, subjects, keypoints.

2.3 Apply the UNSW recipe to the ADR-150 encoder — ACCEPTED (amends ADR-150 §2.3)

• Pretraining corpus: start from the same 14 public datasets (1.3M samples) + our home/MM-Fi frames; data aggregation takes priority over architecture work.
• Tokenization: 80% masking, (30,3)-class small patches; encoder stays ViT-Small-class (~15M params) — F3 and our own DANN/transformer results agree that capacity does not pay.
• The published log-linear scaling (unsaturated) sets the expectation: more heterogeneous CSI in, better zero-shot out.

2.4 Hardware watch items — ACCEPTED (no code now)

• 802.11bf: track silicon/certification; revisit when any commodity chipset exposes standardized sensing measurements. Our opportunistic CSI extraction remains the mechanism until then.
• esp_wifi_sensing: benchmark our presence pipeline against the vendor FSM (one afternoon; useful external baseline). Do not treat as drop-in (refuted claim).
• ZTECSITool AP: optional high-resolution anchor node for the ADR-029 multistatic mesh — procurement-gated; only pursue if a 160 MHz anchor materially helps tomography.

2.5 Explicitly NOT adopted

• No pivot toward "wireless foundation model" papers that don't ship WiFi-CSI artifacts (HeterCSI, FMCW pilot, surveys).
• No DensePose-UV work item: the field has not demonstrated UV regression from commodity WiFi; keypoints remain our supervised target (F5).

3. Consequences

Positive: the calibration system gains the one mechanism (geometry conditioning) the 2026 literature identifies as the difference between layout-brittle and layout-robust supervised WiFi pose; ADR-150 gets a measured training recipe instead of a guessed one; we acquire two external benchmarks (WiFlow-STD, PerceptAlign dataset) to keep our claims honest.

Negative / risks: geometry records add schema surface to banks (mitigated: optional + versioned); every adopted number is preprint-grade until our own benchmark runs land (mitigated by §2.2's no-citation rule); PerceptAlign dataset license is unconfirmed (gated); name collision risk in docs (mitigated: "WiFlow-STD (DY2434)" naming rule).

Re-check by 2026-12: 802.11bf silicon, esp_wifi_sensing maturity (v0.1.x today), and the preprint field (newest source Apr 2026).

4. Open questions (carried from the research run)

1. Does WiFlow-STD retain accuracy when fine-tuned on ESP32-S3/C6 CSI (fewer subcarriers, lower SNR), scored on our 17-keypoint set? (§2.2 answers this.)
2. Is the PerceptAlign dataset downloadable under a usable license, and does the two-checkerboard procedure work with ESP32 transceiver geometry? (§2.1.4 gate.)
3. Will esp_wifi_sensing evolve toward 802.11bf compliance, replacing opportunistic CSI extraction?

5. Source register (evidence-graded)

Source	Type	Used for	Grade
arXiv 2601.12252 (PerceptAlign, MobiCom'26)	preprint+acceptance	F1, §2.1	CLAIMED numbers; failure mode corroborated
arXiv 2602.08661 + DY2434 repo (WiFlow-STD)	preprint + code	F2, §2.2	numbers CLAIMED; artifacts MEASURED
arXiv 2511.18792 (UNSW MAE)	preprint	F3, §2.3	ablations MEASURED in-study; pose transfer hypothesis
IEEE SA 802.11bf-2025 record	standards body	F4, §2.4	MEASURED
Espressif component registry + esp-csi repo	vendor	F4, §2.4	MEASURED; "drop-in" REFUTED 0-3
arXiv 2506.16957 + ZTE repo (ZTECSITool)	vendor preprint + code	F4, §2.4	capability CLAIMED; code MEASURED
arXiv 2601.18200 (HeterCSI), OpenReview LMufK3vzE5 (FMCW pilot), arXiv 2509.15258 (survey)	preprints	F5, §2.5 (screened out)	MEASURED (full-text inspection)