# Continuous-Time Human Motion Field from Events

arXiv: https://arxiv.org/abs/2412.01747 · HTML: https://mlsanity.com/paper/2412.01747/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos](https://mlsanity.com/paper/2403.17346/) (2024)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [Generative Tweening: Long-term Inbetweening of 3D Human Motions](https://mlsanity.com/paper/2005.08891/) (2020)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [Highly Efficient 3D Human Pose Tracking from Events with Spiking Spatiotemporal Transformer](https://mlsanity.com/paper/2303.09681/) (2023)
- [Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data](https://mlsanity.com/paper/1809.08625/) (2018)

## Built on by (most cited first, 1 of 1)

- [LC-Flow: Learning Local Continuous Optical Flow and Confidence from events](https://mlsanity.com/paper/2605.24604/) (2026)

## Abstract (arXiv)

This paper addresses the challenges of estimating a continuous-time human motion field from a stream of events. Existing Human Mesh Recovery (HMR) methods rely predominantly on frame-based approaches, which are prone to aliasing and inaccuracies due to limited temporal resolution and motion blur. In this work, we predict a continuous-time human motion field directly from events by leveraging a recurrent feed-forward neural network to predict human motion in the latent space of possible human motions. Prior state-of-the-art event-based methods rely on computationally intensive optimization across a fixed number of poses at high frame rates, which becomes prohibitively expensive as we increase the temporal resolution. In comparison, we present the first work that replaces traditional discrete-time predictions with a continuous human motion field represented as a time-implicit function, enabling parallel pose queries at arbitrary temporal resolutions. Despite the promises of event cameras, few benchmarks have tested the limit of high-speed human motion estimation. We introduce Beam-splitter Event Agile Human Motion Dataset-a hardware-synchronized high-speed human dataset to fill this gap. On this new data, our method improves joint errors by 23.8% compared to previous event human methods while reducing the computational time by 69%.