4babb320bf
Records the decision to use MM-Fi as primary training dataset and XRF55 as secondary, with a teacher-student pipeline for generating DensePose UV pseudo-labels from paired RGB frames. https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4
144 lines
6.3 KiB
Markdown
144 lines
6.3 KiB
Markdown
# ADR-015: Public Dataset Strategy for Trained Pose Estimation Model
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## Status
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Proposed
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## Context
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The WiFi-DensePose system has a complete model architecture (`DensePoseHead`,
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`ModalityTranslationNetwork`, `WiFiDensePoseRCNN`) and signal processing pipeline,
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but no trained weights. Without a trained model, pose estimation produces random
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outputs regardless of input quality.
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Training requires paired data: simultaneous WiFi CSI captures alongside ground-truth
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human pose annotations. Collecting this data from scratch requires months of effort
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and specialized hardware (multiple WiFi nodes + camera + motion capture rig). Several
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public datasets exist that can bootstrap training without custom collection.
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### The Teacher-Student Constraint
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The CMU "DensePose From WiFi" paper (2023) trains using a teacher-student approach:
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a camera-based RGB pose model (e.g. Detectron2 DensePose) generates pseudo-labels
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during training, so the WiFi model learns to replicate those outputs. At inference,
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the camera is removed. This means any dataset that provides *either* ground-truth
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pose annotations *or* synchronized RGB frames (from which a teacher can generate
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labels) is sufficient for training.
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## Decision
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Use MM-Fi as the primary training dataset, supplemented by XRF55 for additional
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diversity, with a teacher-student pipeline for any dataset that lacks dense pose
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annotations but provides RGB video.
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### Primary Dataset: MM-Fi
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**Paper:** "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless
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Sensing" (NeurIPS 2023 Datasets Track)
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**Repository:** https://github.com/ybCliff/MM-Fi
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**Size:** 40 volunteers × 27 action classes × ~320,000 frames
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**Modalities:** WiFi CSI, mmWave radar, LiDAR, RGB-D, IMU
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**CSI format:** 3 Tx × 3 Rx antennas, 114 subcarriers, 100 Hz sampling rate,
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IEEE 802.11n 5 GHz, raw amplitude + phase
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**Pose annotations:** 17-keypoint COCO skeleton (from RGB-D ground truth)
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**License:** CC BY-NC 4.0
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**Why primary:** Largest public WiFi CSI + pose dataset; raw amplitude and phase
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available (not just processed features); antenna count (3×3) is compatible with the
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existing `CSIProcessor` configuration; COCO keypoints map directly to the
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`KeypointHead` output format.
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### Secondary Dataset: XRF55
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**Paper:** "XRF55: A Radio-Frequency Dataset for Human Indoor Action Recognition"
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(ACM MM 2023)
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**Repository:** https://github.com/aiotgroup/XRF55
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**Size:** 55 action classes, multiple subjects and environments
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**CSI format:** WiFi CSI + UWB radar, 3 Tx × 3 Rx, 30 subcarriers
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**Pose annotations:** Skeleton keypoints from Kinect
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**License:** Research use
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**Why secondary:** Different environments and action vocabulary increase
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generalization; 30 subcarriers requires subcarrier interpolation to match the
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existing 56-subcarrier config.
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### Excluded Datasets and Reasons
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| Dataset | Reason for exclusion |
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|---------|---------------------|
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| RF-Pose / RF-Pose3D (MIT) | Uses 60 GHz mmWave, not 2.4/5 GHz WiFi CSI; incompatible signal physics |
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| Person-in-WiFi (CMU 2019) | Amplitude only, no phase; not publicly released |
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| Widar 3.0 | Gesture recognition only, no full-body pose |
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| NTU-Fi | Activity labels only, no pose keypoints |
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| WiPose | Limited release; superseded by MM-Fi |
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## Implementation Plan
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### Phase 1: MM-Fi Loader
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Implement a `PyTorch Dataset` class that:
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- Reads MM-Fi's HDF5/numpy CSI files
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- Resamples from 114 subcarriers → 56 subcarriers (linear interpolation along
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frequency axis) to match the existing `CSIProcessor` config
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- Normalizes amplitude and unwraps phase using the existing `PhaseSanitizer`
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- Returns `(amplitude, phase, keypoints_17)` tuples
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### Phase 2: Teacher-Student Labels
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For samples where only skeleton keypoints are available (not full DensePose UV maps):
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- Run Detectron2 DensePose on the paired RGB frames to generate `(part_labels,
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u_coords, v_coords)` pseudo-labels
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- Cache generated labels to avoid recomputation during training epochs
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- This matches the training procedure in the original CMU paper
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### Phase 3: Training Pipeline
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- **Loss:** Combined keypoint heatmap loss (MSE) + DensePose part classification
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(cross-entropy) + UV regression (Smooth L1) + transfer loss against teacher
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RGB backbone features
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- **Optimizer:** Adam, lr=1e-3, milestones at 48k and 96k steps (paper schedule)
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- **Hardware:** Single GPU (RTX 3090 or A100); MM-Fi fits in ~50 GB disk
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- **Checkpointing:** Save every epoch; keep best-by-validation-PCK
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### Phase 4: Evaluation
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- **Keypoints:** PCK@0.2 (Percentage of Correct Keypoints within 20% of torso size)
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- **DensePose:** GPS (Geodesic Point Similarity) and GPSM with segmentation mask
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- **Held-out split:** MM-Fi subjects 33-40 (20%) for validation; no test-set leakage
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## Subcarrier Mismatch: MM-Fi (114) vs System (56)
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MM-Fi captures 114 subcarriers at 5 GHz with 40 MHz bandwidth. The existing system
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is configured for 56 subcarriers. Resolution options in order of preference:
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1. **Interpolate MM-Fi → 56** (recommended for initial training): linear interpolation
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preserves spectral envelope, fast, no architecture change needed
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2. **Reconfigure system → 114**: change `CSIProcessor` config; requires re-running
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`verify.py --generate-hash` to update proof hash
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3. **Train at native 114, serve at 56**: separate train/inference configs; adds
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complexity
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Option 1 is chosen for Phase 1 to unblock training immediately.
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## Consequences
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**Positive:**
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- Unblocks end-to-end training without hardware collection
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- MM-Fi's 3×3 antenna setup matches this system's target hardware (ESP32 mesh, ADR-012)
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- 40 subjects with 27 action classes provides reasonable diversity for a first model
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- CC BY-NC license is compatible with research and internal use
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**Negative:**
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- CC BY-NC prohibits commercial deployment of weights trained solely on MM-Fi;
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custom data collection required before commercial release
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- 114→56 subcarrier interpolation loses some frequency resolution; acceptable for
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initial training, revisit in Phase 2
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- MM-Fi was captured in controlled lab environments; expect accuracy drop in
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complex real-world deployments until fine-tuned on domain-specific data
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## References
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- He et al., "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset" (NeurIPS 2023)
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- Yang et al., "DensePose From WiFi" (arXiv 2301.00250, CMU 2023)
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- ADR-012: ESP32 CSI Sensor Mesh (hardware target)
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- ADR-013: Feature-Level Sensing on Commodity Gear
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- ADR-014: SOTA Signal Processing Algorithms
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