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Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition
3D skeleton-based gait emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data. This paper proposes SV-GCN, a single-stream multi-feature fusion framework with temporal invariance. We introduce intra-frame relative motion features to eliminate frame-rate sensitivity and embed heterogeneous cues at shallow layers, enabling early fusion without multi-stream complexity. For variable-length sequences, we design a global mask-guided valid-frame spatio-temporal graph convolution module, introducing frame-rate insensitivity for the first time in t
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T15:11:51.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.