  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.
