COMPASS: Fusion-Matched Supervision for Missing-Modality Human Sensing
Multimodal human activity recognition (HAR) and human pose estimation (HPE) must cope with modalities missing at inference. Completions generated for a fixed fusion model must preserve the fusion readout: what fusion uses from each modality. We propose COMPASS, a completion-and-fusion framework in which each modality occupies a fixed slot filled by an observed representation or a completion inferred from available inputs. Its core principle, fusion-matched supervision, supervises completions with same-sample real targets at readout granularity: token means for HAR, where fusion averages tokens, and per-joint representations for HPE, where fusion preserves joint structure. Trained with com...
| Authors | Hao Wang, Yanyu Qian, Pengcheng Weng et al. |
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| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 24 September 2026 |
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