Proposed

The WiFi-DensePose system has a complete model architecture (`DensePoseHead`,

`ModalityTranslationNetwork`, `WiFiDensePoseRCNN`) and signal processing pipeline,

but no trained weights. Without a trained model, pose estimation produces random

outputs regardless of input quality.

Training requires paired data: simultaneous WiFi CSI captures alongside ground-truth

human pose annotations. Collecting this data from scratch requires months of effort

and specialized hardware (multiple WiFi nodes + camera + motion capture rig). Several

public datasets exist that can bootstrap training without custom collection.

The CMU "DensePose From WiFi" paper (2023) trains using a teacher-student approach:

a camera-based RGB pose model (e.g. Detectron2 DensePose) generates pseudo-labels

during training, so the WiFi model learns to replicate those outputs. At inference,

the camera is removed. This means any dataset that provides *either* ground-truth

pose annotations *or* synchronized RGB frames (from which a teacher can generate

labels) is sufficient for training.

Use MM-Fi as the primary training dataset, supplemented by XRF55 for additional

diversity, with a teacher-student pipeline for any dataset that lacks dense pose

annotations but provides RGB video.

**Paper:** "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless

Sensing" (NeurIPS 2023 Datasets Track)

**Repository:** https://github.com/ybCliff/MM-Fi

**Size:** 40 volunteers × 27 action classes × ~320,000 frames

**Modalities:** WiFi CSI, mmWave radar, LiDAR, RGB-D, IMU

**CSI format:** 3 Tx × 3 Rx antennas, 114 subcarriers, 100 Hz sampling rate,

IEEE 802.11n 5 GHz, raw amplitude + phase

**Pose annotations:** 17-keypoint COCO skeleton (from RGB-D ground truth)

**License:** CC BY-NC 4.0

**Why primary:** Largest public WiFi CSI + pose dataset; raw amplitude and phase

available (not just processed features); antenna count (3×3) is compatible with the

existing `CSIProcessor` configuration; COCO keypoints map directly to the

`KeypointHead` output format.

**Paper:** "XRF55: A Radio-Frequency Dataset for Human Indoor Action Recognition"

(ACM MM 2023)

**Repository:** https://github.com/aiotgroup/XRF55

**Size:** 55 action classes, multiple subjects and environments

**CSI format:** WiFi CSI + UWB radar, 3 Tx × 3 Rx, 30 subcarriers

**Pose annotations:** Skeleton keypoints from Kinect

**License:** Research use

**Why secondary:** Different environments and action vocabulary increase

generalization; 30 subcarriers requires subcarrier interpolation to match the

existing 56-subcarrier config.

| Dataset | Reason for exclusion |

|---------|---------------------|

| RF-Pose / RF-Pose3D (MIT) | Uses 60 GHz mmWave, not 2.4/5 GHz WiFi CSI; incompatible signal physics |

| Person-in-WiFi (CMU 2019) | Amplitude only, no phase; not publicly released |

| Widar 3.0 | Gesture recognition only, no full-body pose |

| NTU-Fi | Activity labels only, no pose keypoints |

| WiPose | Limited release; superseded by MM-Fi |

Implement a `PyTorch Dataset` class that:

- Reads MM-Fi's HDF5/numpy CSI files

- Resamples from 114 subcarriers → 56 subcarriers (linear interpolation along

frequency axis) to match the existing `CSIProcessor` config

- Normalizes amplitude and unwraps phase using the existing `PhaseSanitizer`

- Returns `(amplitude, phase, keypoints_17)` tuples

For samples where only skeleton keypoints are available (not full DensePose UV maps):

- Run Detectron2 DensePose on the paired RGB frames to generate `(part_labels,

u_coords, v_coords)` pseudo-labels

- Cache generated labels to avoid recomputation during training epochs

- This matches the training procedure in the original CMU paper

- **Loss:** Combined keypoint heatmap loss (MSE) + DensePose part classification

(cross-entropy) + UV regression (Smooth L1) + transfer loss against teacher

RGB backbone features

- **Optimizer:** Adam, lr=1e-3, milestones at 48k and 96k steps (paper schedule)

- **Hardware:** Single GPU (RTX 3090 or A100); MM-Fi fits in ~50 GB disk

- **Checkpointing:** Save every epoch; keep best-by-validation-PCK

- **Keypoints:** [email protected] (Percentage of Correct Keypoints within 20% of torso size)

- **DensePose:** GPS (Geodesic Point Similarity) and GPSM with segmentation mask

- **Held-out split:** MM-Fi subjects 33-40 (20%) for validation; no test-set leakage

MM-Fi captures 114 subcarriers at 5 GHz with 40 MHz bandwidth. The existing system

is configured for 56 subcarriers. Resolution options in order of preference:

1. **Interpolate MM-Fi → 56** (recommended for initial training): linear interpolation

preserves spectral envelope, fast, no architecture change needed

2. **Reconfigure system → 114**: change `CSIProcessor` config; requires re-running

`verify.py --generate-hash` to update proof hash

3. **Train at native 114, serve at 56**: separate train/inference configs; adds

complexity

Option 1 is chosen for Phase 1 to unblock training immediately.

**Positive:**

- Unblocks end-to-end training without hardware collection

- MM-Fi's 3×3 antenna setup matches this system's target hardware (ESP32 mesh, ADR-012)

- 40 subjects with 27 action classes provides reasonable diversity for a first model

- CC BY-NC license is compatible with research and internal use

**Negative:**

- CC BY-NC prohibits commercial deployment of weights trained solely on MM-Fi;

custom data collection required before commercial release

- 114→56 subcarrier interpolation loses some frequency resolution; acceptable for

initial training, revisit in Phase 2

- MM-Fi was captured in controlled lab environments; expect accuracy drop in

complex real-world deployments until fine-tuned on domain-specific data

- He et al., "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset" (NeurIPS 2023)

- Yang et al., "DensePose From WiFi" (arXiv 2301.00250, CMU 2023)

- ADR-012: ESP32 CSI Sensor Mesh (hardware target)

- ADR-013: Feature-Level Sensing on Commodity Gear

- ADR-014: SOTA Signal Processing Algorithms