Published and submitted research by Weiping Yan, with official publication and submission links.
This page lists one formally published paper and one manuscript submitted to DAI 2026. The submitted manuscript is under review and its decision is pending; it is not yet accepted or published. The 2023 independent study used randomly generated, synthetic data; it did not analyze clinical records or real-world environmental observations. Official titles and metadata are retained in their original language.
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.
@unpublished{yan2026temporac,title={{TempoRAC}: Identity-Indexed Local Tempo Routing for Multi-Person Repetitive Action Counting},author={Yan, Weiping and Zhu, Jiayu and Yan, Xu},year={2026},month=sep,note={Submitted to {ICASSP} 2027; under review. Equal contribution by all three authors.},}
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.
@unpublished{fei2026vchsegformer,title={{VCH-SegFormer} for Street-View Vegetation Mapping and Assessing the Associations Between Urban Vegetation and {PM2.5}},author={Fei, Xiaoyan and Yan, Weiping and Zhu, Qi and Yan, Xu and Wu, Zherong},year={2026},month=oct,note={Submitted to {IEEE} Transactions on Geoscience and Remote Sensing; under review.},}
@unpublished{wang2026racert,title={{RACER-T}: Temporal Density Modeling for Pose-Based Repetitive Action Counting},author={Wang, Shuoran and Yan, Weiping and Wang, Xiang},year={2026},month=oct,note={Submitted to Signal, Image and Video Processing (Springer Nature); under review. Opted in to the In Review preprint service on Research Square.},}
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.
@unpublished{wang2026oamga,title={{OAM-GA}: Reliability-Guided Motion Completion for Occlusion-RobustGaussian Avatars},author={Wang, Xiang and Yan, Xu and Pei, Letian and Yan, Weiping},year={2026},note={Submitted to {DAI} 2026; under review, decision pending, and not yet accepted or published.},url={https://openreview.net/forum?id=UV2UJ4VHf7},}
@article{yan2023pm25,title={Effect of {PM2.5} air pollution on the incidence of respiratory diseases: A {Python}-based data analysis},author={Yan, Weiping},journal={Theoretical and Natural Science},volume={8},number={1},pages={70--75},year={2023},month=nov,publisher={EWA Publishing},doi={10.54254/2753-8818/8/20240361},url={https://tns.ewapub.com/article/view/6279},}