# Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data

arXiv: https://arxiv.org/abs/1806.09174 · HTML: https://mlsanity.com/paper/1806.09174/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural Networks](https://mlsanity.com/paper/2202.01727/) (2022)
- [F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning](https://mlsanity.com/paper/2401.08609/) (2023)
- [SSSUMO: Real-Time Semi-Supervised Submovement Decomposition](https://mlsanity.com/paper/2507.08028/) (2025)

## Abstract (arXiv)

Semantic segmentation of motion capture sequences plays a key part in many data-driven motion synthesis frameworks. It is a preprocessing step in which long recordings of motion capture sequences are partitioned into smaller segments. Afterwards, additional methods like statistical modeling can be applied to each group of structurally-similar segments to learn an abstract motion manifold. The segmentation task however often remains a manual task, which increases the effort and cost of generating large-scale motion databases. We therefore propose an automatic framework for semantic segmentation of motion capture data using a dilated temporal fully-convolutional network. Our model outperforms a state-of-the-art model in action segmentation, as well as three networks for sequence modeling. We further show our model is robust against high noisy training labels.