Research
Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition
arXiv:2609.11680v1 Announce Type: new Abstract: 3D skeleton-based gait emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data. This paper propose
arXiv:2609.11680v1 Announce Type: new Abstract: 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 this domain. On the E-Gait dataset, our method achieves performance comparable to state-of-the-art while demonstrating strong generalization across varying sequence lengths and frame rates, offering a viable pathway for pre-training on large-scale skeleton-based action recognition datasets.
Related
- Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization
- NeuroPath: Brain-Inspired Dual-Pathway Graph Convolutional Networks for Skeleton-Based Action Recognition
- Generative Data Augmentation for Skeleton Action Recognition
Source: arXiv cs.CV | 2026-09-11