CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators

Harry Zhang, Luca Carlone

Keywords: Human pose estimation, 3d vision, uncertainty quantification

TL;DR: A novel method for estimating 3D human pose from RGB videos with uncertainty certification

Abstract: 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 end-to-end 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.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 4942