# A Plug-and-Play Multi-Criteria Guidance for Diverse In-Betweening Human Motion Generation

arXiv: https://arxiv.org/abs/2508.01590 · HTML: https://mlsanity.com/paper/2508.01590/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 54 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [Diffusion Models already have a Semantic Latent Space](https://mlsanity.com/paper/2210.10960/) (2022)
- [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [DLow: Diversifying Latent Flows for Diverse Human Motion Prediction](https://mlsanity.com/paper/2003.08386/) (2020)
- [Convolutional Autoencoders for Human Motion Infilling](https://mlsanity.com/paper/2010.11531/) (2020)
- [Flexible Motion In-betweening with Diffusion Models](https://mlsanity.com/paper/2405.11126/) (2024)
- [MultiAct: Long-Term 3D Human Motion Generation from Multiple Action Labels](https://mlsanity.com/paper/2212.05897/) (2022)
- [Interaction Transformer for Human Reaction Generation](https://mlsanity.com/paper/2207.01685/) (2022)
- [Real-time Controllable Motion Transition for Characters](https://mlsanity.com/paper/2205.02540/) (2022)
- [Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction](https://mlsanity.com/paper/2205.15608/) (2022)
- [Conditional Motion In-betweening](https://mlsanity.com/paper/2202.04307/) (2022)
- [Generative Tweening: Long-term Inbetweening of 3D Human Motions](https://mlsanity.com/paper/2005.08891/) (2020)
- [Prompt Evolution for Generative AI: A Classifier-Guided Approach](https://mlsanity.com/paper/2305.16347/) (2023)

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

- [Training-free Controllable Human Motion Generation under Heterogeneous Constraints](https://mlsanity.com/paper/2607.01990/) (2026)

## Abstract (arXiv)

In-betweening human motion generation aims to synthesize intermediate motions that transition between user-specified keyframes. In addition to maintaining smooth transitions, a crucial requirement of this task is to generate diverse motion sequences. It is still challenging to maintain diversity, particularly when it is necessary for the motions within a generated batch sampling to differ meaningfully from one another due to complex motion dynamics. In this paper, we propose a novel method, termed the Multi-Criteria Guidance with In-Betweening Motion Model (MCG-IMM), for in-betweening human motion generation. A key strength of MCG-IMM lies in its plug-and-play nature: it enhances the diversity of motions generated by pretrained models without introducing additional parameters This is achieved by providing a sampling process of pretrained generative models with multi-criteria guidance. Specifically, MCG-IMM reformulates the sampling process of pretrained generative model as a multi-criteria optimization problem, and introduces an optimization process to explore motion sequences that satisfy multiple criteria, e.g., diversity and smoothness. Moreover, our proposed plug-and-play multi-criteria guidance is compatible with different families of generative models, including denoised diffusion probabilistic models, variational autoencoders, and generative adversarial networks. Experiments on four popular human motion datasets demonstrate that MCG-IMM consistently state-of-the-art methods in in-betweening motion generation task.