多视角三维姿态估计
多视角学习在三维感知中具有重要价值，其通过融合多源视觉信息突破单视角的感知局限，为复杂场景下的三维重建和姿态估计等任务提供更鲁棒、高精度的解决方案。该技术可赋能虚拟现实交互、自动驾驶感知、机器人导航等多元化场景。例如，在舞蹈动作智能分析中，借助无标定的多视角学习，无需依赖复杂校准设备即可生成高精度三维姿态，显著降低教学成本并实现个性化反馈；在动态人机协作场景中，校准无关的多视角手部重建技术能够实时捕捉精细手势，助力机器人理解复杂操作意图，提升协作效率与安全性。我们研究聚焦于无标定下的多视角人体/手部姿态估计和三维重建，通过设计自适应语义对齐框架与噪声鲁棒的跨视角融合策略，突破传统方法对相机参数的强依赖，并显著提升模型精度和鲁棒性。
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Unified 2D-3D Discrete Priors for Noise-Robust and Calibration-Free Multiview 3D Human Pose Estimation
Abstract Multi-view 3D human pose estimation (HPE) leverages complementary information across views to improve accuracy and robustness. Traditional methods rely on camera calibration to establish geometric correspondences, which is sensitive to calibration accuracy and lacks flexibility in dynamic settings. Calibration-free approaches address these limitations by learning adaptive view interactions, typically leveraging expressive and flexible continuous […]