# Evaluating the Evaluators: Towards Human-aligned Metrics for Missing Markers Reconstruction

arXiv: https://arxiv.org/abs/2410.14334 · HTML: https://mlsanity.com/paper/2410.14334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [What is the Best Automated Metric for Text to Motion Generation?](https://mlsanity.com/paper/2309.10248/) (2023)
- [Graph-based Normalizing Flow for Human Motion Generation and Reconstruction](https://mlsanity.com/paper/2104.03020/) (2021)
- [Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization](https://mlsanity.com/paper/2012.02043/) (2020)
- [A Locality-based Neural Solver for Optical Motion Capture](https://mlsanity.com/paper/2309.00428/) (2023)

## Abstract (arXiv)

Animation data is often obtained through optical motion capture systems, which utilize a multitude of cameras to establish the position of optical markers. However, system errors or occlusions can result in missing markers, the manual cleaning of which can be time-consuming. This has sparked interest in machine learning-based solutions for missing marker reconstruction in the academic community. Most academic papers utilize a simplistic mean square error as the main metric. In this paper, we show that this metric does not correlate with subjective perception of the fill quality. Additionally, we introduce and evaluate a set of better-correlated metrics that can drive progress in the field.