# Real-time Diverse Motion In-betweening with Space-time Control

arXiv: https://arxiv.org/abs/2410.00270 · HTML: https://mlsanity.com/paper/2410.00270/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [OmniControl: Control Any Joint at Any Time for Human Motion Generation](https://mlsanity.com/paper/2310.08580/) (2023)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases](https://mlsanity.com/paper/2201.04439/) (2022)
- [Flexible Motion In-betweening with Diffusion Models](https://mlsanity.com/paper/2405.11126/) (2024)
- [Recurrent Transition Networks for Character Locomotion](https://mlsanity.com/paper/1810.02363/) (2018)
- [MoGlow: Probabilistic and controllable motion synthesis using normalising flows](https://mlsanity.com/paper/1905.06598/) (2019)
- [Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer](https://mlsanity.com/paper/2406.06508/) (2024)
- [Real-time Controllable Motion Transition for Characters](https://mlsanity.com/paper/2205.02540/) (2022)
- [Motion In-Betweening with Phase Manifolds](https://mlsanity.com/paper/2308.12751/) (2023)
- [GANimator: Neural Motion Synthesis from a Single Sequence](https://mlsanity.com/paper/2205.02625/) (2022)
- [RSMT: Real-time Stylized Motion Transition for Characters](https://mlsanity.com/paper/2306.11970/) (2023)

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

- [Towards Robust and Controllable Text-to-Motion via Masked Autoregressive Diffusion](https://mlsanity.com/paper/2505.11013/) (2025)
- [SILK: Smooth InterpoLation frameworK for motion in-betweening A Simplified Computational Approach](https://mlsanity.com/paper/2506.09075/) (2025)
- [PMG: Progressive Motion Generation via Sparse Anchor Postures Curriculum Learning](https://mlsanity.com/paper/2504.16722/) (2025)
- [Motion In-Betweening for Densely Interacting Characters](https://mlsanity.com/paper/2510.00314/) (2025)

## Abstract (arXiv)

In this work, we present a data-driven framework for generating diverse in-betweening motions for kinematic characters. Our approach injects dynamic conditions and explicit motion controls into the procedure of motion transitions. Notably, this integration enables a finer-grained spatial-temporal control by allowing users to impart additional conditions, such as duration, path, style, etc., into the in-betweening process. We demonstrate that our in-betweening approach can synthesize both locomotion and unstructured motions, enabling rich, versatile, and high-quality animation generation.