Weiping Yan

Multi-person repetitive action counting (MRAC) is crucial for human-centric applications such as fitness analysis and sports monitoring. However, existing methods suffer from asynchronous motions, varying tempos, and unreliable counting under occlusions. To address these challenges, we propose TempoRAC, a pose-driven framework for adaptive person-wise repetition counting. TempoRAC introduces an identity-conditioned pose encoder to capture individual motion patterns, and a tempo-aware routing module that dynamically selects fast, medium, and slow experts for modeling non-stationary repetitions. Furthermore, a normalized overlap-add fusion strategy integrates window responses and enables consistent identity-wise decoding without redundant counting. Extensive experiments demonstrate that TempoRAC achieves robust and accurate person-wise repetition counting under diverse motion conditions.

Urban vegetation can influence fine particulate matter (PM2.5) concentrations through particle deposition and changes in atmospheric dispersion, while observed associations also reflect land-use patterns. However, assessments of urban vegetation–PM2.5 associations rely predominantly on satellite-derived spectral indices, while street-level greenness remains comparatively underexamined. We developed VCH-SegFormer, a semantic segmentation model that extends SegFormer with a Vertical Context Gate and a Hierarchical Vegetation Head to distinguish trees from low vegetation. With a MiT-B5 encoder, the model achieved a mean intersection over union (mIoU) of 79.3% and a vegetation-fraction mean absolute error of 0.032 on ADE20K-6, and an mIoU of 86.3% on Cityscapes. The trained models were applied to 430,676 street-view images in Guangzhou, with the resulting predictions aggregated into one-kilometer cells to compare street-view green view index (GVI) and satellite normalized difference vegetation index (NDVI) in relation to modeled PM2.5. Vegetation GVI and NDVI showed Pearson correlations of −0.242 and −0.419, respectively, with modeled PM2.5. In a complementary U.S. analysis of 430 monitors, a one-standard-deviation increase in NDVI was associated with a 0.29 μg/m³ lower measured PM2.5 concentration. In a separate sample of 55 monitors, a one-standard-deviation increase in street-view GVI was associated with a 0.70 μg/m³ lower measured PM2.5 concentration after adjustment for NDVI and other covariates. The approach combines street-view vegetation segmentation with satellite observations to examine how eye-level and overhead greenness relate to PM2.5.

Reconstructing sharp and animatable 3D human avatars from motion-blurred videos remains challenging under partial occlusion, where unreliable SMPL estimates can propagate pose errors into Gaussian deformation. To address this problem, we propose OAM-GA, an occlusion-robust framework for motion-aware Gaussian avatar reconstruction. OAM-GA estimates joint-wise reliability and restores missing motion using visible temporal context. Specifically, a Bidirectional Wavelet Masked Motion Completion module decomposes motion features into low- and high-frequency components and reconstructs contiguous occluded intervals from observations before and after the masked region. Clean–occluded contrastive consistency learning further encourages occlusion-invariant motion representations, while reliability-guided residual refinement selectively corrects uncertain joints and preserves reliable ones. Experiments on BlurZJU and BS-Human show that OAM-GA improves overall reconstruction robustness and perceptual quality under diverse occlusion patterns while maintaining the rendering fidelity of the original baseline.