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MarkerNet: A Divide-and-Conquer Solution to Motion Capture Solving From Raw Markers
Zhipeng Hu, Jilin Tang, Lincheng Li, Jie Hou, Haoran Xin, Xin Yu, Jiajun Bu
Decomposes full-body mocap solving into local-part sub-motions aggregated by a graph network, reducing the costly manual labeling step.
Abstract
Marker‐based optical motion capture (MoCap) aims to localize 3D human motions from a sequence of input raw markers. It is widely used to produce physical movements for virtual characters in various games such as the role‐playing game, the fighting game, and the action‐adventure game. However, the conventional MoCap cleaning and solving process is extremely labor‐intensive, time‐consuming, and usually the most costly part of game animation production. Thus, there is a high demand for automated algorithms to replace costly manual operations and achieve accurate MoCap cleaning and solving in the game industry. In this article, we design a divide‐and‐conquer‐based MoCap solving network, dubbed MarkerNet, to estimate human skeleton motions from sequential raw markers effectively. In a nutshell, our key idea is to decompose the task of direct solving of global motion from all markers into first modeling sub‐motions of local parts from the corresponding marker subsets and then aggregating sub‐motions into a global one. In this manner, our model can effectively capture local motion patterns w.r.t. different marker subsets, thus producing more accurate results compared to the existing methods. Extensive experiments on both real and synthetic data verify the effectiveness of the proposed method.
How to read this
- Category
- Method: a learned motion-capture solving network
- Contributions
- MarkerNet, a divide-and-conquer network that estimates skeleton motion from raw optical markers
- Decomposes global solving into local-part sub-motions from marker subsets, then aggregates them
- Aims to automate the labor-intensive MoCap cleaning and solving step in game production
- Context
- Targets automated optical MoCap solving for game animation, building on marker-cleanup lineage such as Perepichka et al.'s kinematic-reference trajectory repair.Builds on: Robust Marker Trajectory Repair for MOCAP Using Kinematic Reference
- Correctness
- The local-then-global decomposition assumes marker subsets map cleanly to body parts; accuracy gains are reported relative to existing solving methods, so a reader should check the evaluation protocol and how labeling effort is actually reduced.
- Clarity
- Reasonably accessible; a first pass conveys the divide-and-conquer idea, a second pass clarifies the graph aggregation and marker-subset design.
- How to read it
- Focus on how marker subsets are defined and how the graph network aggregates sub-motions; a second pass is worth it for the architecture and accuracy comparison.
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