# Conditional Motion In-betweening

arXiv: https://arxiv.org/abs/2202.04307 · HTML: https://mlsanity.com/paper/2202.04307/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 55 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [Learning Joint Spatial-Temporal Transformations for Video Inpainting](https://mlsanity.com/paper/2007.10247/) (2020)
- [A Spatio-temporal Transformer for 3D Human Motion Prediction](https://mlsanity.com/paper/2004.08692/) (2020)
- [Text2Action: Generative Adversarial Synthesis from Language to Action](https://mlsanity.com/paper/1710.05298/) (2017)
- [Structured Prediction Helps 3D Human Motion Modelling](https://mlsanity.com/paper/1910.09070/) (2019)
- [Unpaired Motion Style Transfer from Video to Animation](https://mlsanity.com/paper/2005.05751/) (2020)
- [Convolutional Autoencoders for Human Motion Infilling](https://mlsanity.com/paper/2010.11531/) (2020)
- [Learning Human Motion Models for Long-term Predictions](https://mlsanity.com/paper/1704.02827/) (2017)
- [Learning Stochastic Recurrent Networks](https://mlsanity.com/paper/1411.7610/) (2014)
- [Single-Shot Motion Completion with Transformer](https://mlsanity.com/paper/2103.00776/) (2021)
- [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)
- [Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers](https://mlsanity.com/paper/2109.07531/) (2021)
- [Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling](https://mlsanity.com/paper/1908.07214/) (2019)
- [HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media](https://mlsanity.com/paper/2110.07235/) (2021)

## Built on by (most cited first, 25 of 25)

- [FLAME: Free-form Language-based Motion Synthesis & Editing](https://mlsanity.com/paper/2209.00349/) (2022)
- [TEACH: Temporal Action Composition for 3D Humans](https://mlsanity.com/paper/2209.04066/) (2022)
- [Seamless Human Motion Composition with Blended Positional Encodings](https://mlsanity.com/paper/2402.15509/) (2024)
- [HaWoR: World-Space Hand Motion Reconstruction from Egocentric Videos](https://mlsanity.com/paper/2501.02973/) (2025)
- [SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation](https://mlsanity.com/paper/2304.10417/) (2023)
- [Visibility Aware Human-Object Interaction Tracking from Single RGB Camera](https://mlsanity.com/paper/2303.16479/) (2023)
- [Interactive Character Control with Auto-Regressive Motion Diffusion Models](https://mlsanity.com/paper/2306.00416/) (2023)
- [Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion](https://mlsanity.com/paper/2312.04466/) (2023)
- [SceneMI: Motion In-betweening for Modeling Human-Scene Interactions](https://mlsanity.com/paper/2503.16289/) (2025)
- [Text-guided 3D Human Motion Generation with Keyframe-based Parallel Skip Transformer](https://mlsanity.com/paper/2405.15439/) (2024)
- [Motion Synthesis with Sparse and Flexible Keyjoint Control](https://mlsanity.com/paper/2503.15557/) (2025)
- [Deep Compositional Phase Diffusion for Long Motion Sequence Generation](https://mlsanity.com/paper/2510.14427/) (2025)
- [MotionBricks: Scalable Real-Time Motions with Modular Latent Generative Model and Smart Primitives](https://mlsanity.com/paper/2604.24833/) (2026)
- [A Unified Masked Autoencoder with Patchified Skeletons for Motion Synthesis](https://mlsanity.com/paper/2308.07301/) (2023)
- [Orientation-Aware Leg Movement Learning for Action-Driven Human Motion Prediction](https://mlsanity.com/paper/2310.14907/) (2023)
- [Learning Joint Representation of Human Motion and Language](https://mlsanity.com/paper/2210.15187/) (2022)
- [Diverse Motion In-betweening with Dual Posture Stitching](https://mlsanity.com/paper/2303.14457/) (2023)
- [SILK: Smooth InterpoLation frameworK for motion in-betweening A Simplified Computational Approach](https://mlsanity.com/paper/2506.09075/) (2025)
- [A Plug-and-Play Multi-Criteria Guidance for Diverse In-Betweening Human Motion Generation](https://mlsanity.com/paper/2508.01590/) (2025)
- [Learning Context-Adaptive Motion Priors for Masked Motion Diffusion Models with Efficient Kinematic Attention Aggregation](https://mlsanity.com/paper/2603.07697/) (2026)
- [PMG: Progressive Motion Generation via Sparse Anchor Postures Curriculum Learning](https://mlsanity.com/paper/2504.16722/) (2025)
- [From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control](https://mlsanity.com/paper/2506.00043/) (2025)
- [Motion In-Betweening for Densely Interacting Characters](https://mlsanity.com/paper/2510.00314/) (2025)
- [Adaptive Interpolation-Synthesis for Motion In-Betweening on Keyframe-Based Animation](https://mlsanity.com/paper/2605.02742/) (2026)
- [Generative Motion In-betweening by Diffusion over Continuous Implicit Representations](https://mlsanity.com/paper/2605.12778/) (2026)

## Abstract (arXiv)

Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such as periodic footstep motion while walking. Although state-of-the-art MIB methods are capable of producing plausible motions given sparse key-poses, they often lack the controllability to generate motions satisfying the semantic contexts required in practical applications. We focus on the method that can handle pose or semantic conditioned MIB tasks using a unified model. We also present a motion augmentation method to improve the quality of pose-conditioned motion generation via defining a distribution over smooth trajectories. Our proposed method outperforms the existing state-of-the-art MIB method in pose prediction errors while providing additional controllability.