# Perpetual Motion: Generating Unbounded Human Motion

arXiv: https://arxiv.org/abs/2007.13886 · HTML: https://mlsanity.com/paper/2007.13886/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 30 of 36)

- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset](https://mlsanity.com/paper/2307.00818/) (2023)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis](https://mlsanity.com/paper/2305.00976/) (2023)
- [MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model](https://mlsanity.com/paper/2404.19759/) (2024)
- [AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism](https://mlsanity.com/paper/2309.00796/) (2023)
- [HumanTOMATO: Text-aligned Whole-body Motion Generation](https://mlsanity.com/paper/2310.12978/) (2023)
- [Optimizing Diffusion Noise Can Serve As Universal Motion Priors](https://mlsanity.com/paper/2312.11994/) (2023)
- [TLControl: Trajectory and Language Control for Human Motion Synthesis](https://mlsanity.com/paper/2311.17135/) (2023)
- [EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation](https://mlsanity.com/paper/2312.02256/) (2023)
- [Compositional Human-Scene Interaction Synthesis with Semantic Control](https://mlsanity.com/paper/2207.12824/) (2022)
- [Seamless Human Motion Composition with Blended Positional Encodings](https://mlsanity.com/paper/2402.15509/) (2024)
- [ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation](https://mlsanity.com/paper/2203.07706/) (2022)
- [The Wanderings of Odysseus in 3D Scenes](https://mlsanity.com/paper/2112.09251/) (2021)
- [ParCo: Part-Coordinating Text-to-Motion Synthesis](https://mlsanity.com/paper/2403.18512/) (2024)
- [MultiAct: Long-Term 3D Human Motion Generation from Multiple Action Labels](https://mlsanity.com/paper/2212.05897/) (2022)
- [MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling](https://mlsanity.com/paper/2409.17686/) (2024)
- [HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects](https://mlsanity.com/paper/2407.12371/) (2024)
- [ReinDiffuse: Crafting Physically Plausible Motions with Reinforced Diffusion Model](https://mlsanity.com/paper/2410.07296/) (2024)
- [InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation](https://mlsanity.com/paper/2407.10061/) (2024)
- [Pay Attention and Move Better: Harnessing Attention for Interactive Motion Generation and Training-free Editing](https://mlsanity.com/paper/2410.18977/) (2024)
- [ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions](https://mlsanity.com/paper/2305.15748/) (2023)
- [Language-guided Human Motion Synthesis with Atomic Actions](https://mlsanity.com/paper/2308.09611/) (2023)
- [A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals](https://mlsanity.com/paper/2404.04890/) (2024)
- [InfiniDreamer: Arbitrarily Long Human Motion Generation via Segment Score Distillation](https://mlsanity.com/paper/2411.18303/) (2024)
- [PaCMO: Partner Dependent Human Motion Generation in Dyadic Human Activity using Neural Operators](https://mlsanity.com/paper/2211.16210/) (2022)
- [Text-driven Human Motion Generation with Motion Masked Diffusion Model](https://mlsanity.com/paper/2409.19686/) (2024)
- [BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis](https://mlsanity.com/paper/2412.00112/) (2024)

## Abstract (arXiv)

The modeling of human motion using machine learning methods has been widely studied. In essence it is a time-series modeling problem involving predicting how a person will move in the future given how they moved in the past. Existing methods, however, typically have a short time horizon, predicting a only few frames to a few seconds of human motion. Here we focus on long-term prediction; that is, generating long sequences (potentially infinite) of human motion that is plausible. Furthermore, we do not rely on a long sequence of input motion for conditioning, but rather, can predict how someone will move from as little as a single pose. Such a model has many uses in graphics (video games and crowd animation) and vision (as a prior for human motion estimation or for dataset creation). To address this problem, we propose a model to generate non-deterministic, textitever-changing, perpetual human motion, in which the global trajectory and the body pose are cross-conditioned. We introduce a novel KL-divergence term with an implicit, unknown, prior. We train this using a heavy-tailed function of the KL divergence of a white-noise Gaussian process, allowing latent sequence temporal dependency. We perform systematic experiments to verify its effectiveness and find that it is superior to baseline methods.