We introduce CHAMP, a novel method for learning sequence-to-sequence, multi-hypothesis 3D human poses from 2D keypoints by leveraging a conditional distribution with a diffusion model. To predict a single output 3D pose sequence, we generate and aggregate multiple 3D pose hypotheses. For better aggregation results, we develop a method to score these hypotheses during training, effectively integrating conformal prediction into the learning process. This process results in a differentiable conformal predictor that is trained end2end with the 3D pose estimator. Post-training, the learned scoring model is used as the conformity score, and the 3D pose estimator is combined with a conformal predictor to select the most accurate hypotheses for downstream aggregation. Our results indicate that using a simple mean aggregation on the conformal prediction-filtered hypotheses set yields competitive results. When integrated with more sophisticated aggregation techniques, our method achieves state-of-the-art performance across various metrics and datasets while inheriting the probabilistic guarantees of conformal prediction.

我们引入了CHAMP，这是一种新颖的方法，通过利用具有扩散模型的条件分布，从2D关键点学习序列到序列、多假设的3D人体姿势。我们生成和聚合多个3D姿势假设，通过在训练期间为这些假设打分的方法，有效地将符合预测集成到学习过程中，从而达到更好的聚合结果。此过程产生了一个可微的符合预测器，与3D姿势估计器一起进行end2end训练。训练后，学习到的打分模型被用作一致性得分，将3D姿势估计器与一致性预测器结合起来，选择最准确的假设进行下游聚合。我们的结果表明，在一致性预测-过滤的假设集上进行简单的平均聚合产生了竞争性的结果。当与更复杂的聚合技术集成时，我们的方法在各种度量和数据集上实现了最先进的性能，并继承了一致性预测的概率保证。

CHAMP: 三维人体多假设姿势估计器的确认化技术