论文标题
推理阶段优化跨阶段3D人姿势估计
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
论文作者
论文摘要
现有的3D人体姿势估计模型在适用于新场景的情况下，由于其可推广性有限，因此绩效下降。在这项工作中，我们提出了一个新颖的框架，推理阶段优化（ISO），以改善源和目标数据来自不同姿势分布时3D姿势模型的普遍性。我们的主要见解是，即使没有标记，目标数据也具有有关其潜在分布的宝贵先验。为了利用此类信息，拟议的ISO在每个单个目标实例上执行几何学意识的自我监督学习（SSL），并在进行预测之前更新3D姿势模型。通过这种方式，该模型可以挖掘有关目标情景的分布知识，并以增强的概括性能迅速适应它。此外，为了处理顺序目标数据，我们提出了一种在线模式，用于通过流式传输SSL实现我们的ISO框架，从而大大提高了其有效性。我们系统地分析了我们的ISO框架为何以及如何在跨幕组设置下的不同基准测试中工作。值得注意的是，它在MPI-INF-3DHP上产生了83.6％3D PCK的新最先进，以前的最佳结果提高了9.7％。代码将发布。
Existing 3D human pose estimation models suffer performance drop when applying to new scenarios with unseen poses due to their limited generalizability. In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions. Our main insight is that the target data, even though not labeled, carry valuable priors about their underlying distribution. To exploit such information, the proposed ISO performs geometry-aware self-supervised learning (SSL) on each single target instance and updates the 3D pose model before making prediction. In this way, the model can mine distributional knowledge about the target scenario and quickly adapt to it with enhanced generalization performance. In addition, to handle sequential target data, we propose an online mode for implementing our ISO framework via streaming the SSL, which substantially enhances its effectiveness. We systematically analyze why and how our ISO framework works on diverse benchmarks under cross-scenario setup. Remarkably, it yields new state-of-the-art of 83.6% 3D PCK on MPI-INF-3DHP, improving upon the previous best result by 9.7%. Code will be released.
