# Implicit Neural Representations for Variable Length Human Motion Generation

arXiv: https://arxiv.org/abs/2203.13694 · HTML: https://mlsanity.com/paper/2203.13694/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [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)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [Learning 3D Human Dynamics from Video](https://mlsanity.com/paper/1812.01601/) (2018)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [Unconstrained Scene Generation with Locally Conditioned Radiance Fields](https://mlsanity.com/paper/2104.00670/) (2021)
- [Image Generators with Conditionally-Independent Pixel Synthesis](https://mlsanity.com/paper/2011.13775/) (2020)
- [Deep representation learning for human motion prediction and classification](https://mlsanity.com/paper/1702.07486/) (2017)
- [Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild](https://mlsanity.com/paper/2009.10013/) (2020)
- [Sim2real transfer learning for 3D human pose estimation: motion to the rescue](https://mlsanity.com/paper/1907.02499/) (2019)
- [Synthetic Humans for Action Recognition from Unseen Viewpoints](https://mlsanity.com/paper/1912.04070/) (2019)
- [3D Human Shape Reconstruction from a Polarization Image](https://mlsanity.com/paper/2007.09268/) (2020)
- [Gradient Origin Networks](https://mlsanity.com/paper/2007.02798/) (2020)
- [GlocalNet: Class-aware Long-term Human Motion Synthesis](https://mlsanity.com/paper/2012.10744/) (2020)
- [CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation](https://mlsanity.com/paper/2005.00754/) (2020)

## Built on by (most cited first, 30 of 61)

- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [MoMask: Generative Masked Modeling of 3D Human Motions](https://mlsanity.com/paper/2312.00063/) (2023)
- [PhysDiff: Physics-Guided Human Motion Diffusion Model](https://mlsanity.com/paper/2212.02500/) (2022)
- [MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model](https://mlsanity.com/paper/2404.19759/) (2024)
- [Inter-X: Towards Versatile Human-Human Interaction Analysis](https://mlsanity.com/paper/2312.16051/) (2023)
- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation](https://mlsanity.com/paper/2401.08559/) (2024)
- [EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation](https://mlsanity.com/paper/2312.02256/) (2023)
- [Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model](https://mlsanity.com/paper/2309.06284/) (2023)
- [Single Motion Diffusion](https://mlsanity.com/paper/2302.05905/) (2023)
- [Large Motion Model for Unified Multi-Modal Motion Generation](https://mlsanity.com/paper/2404.01284/) (2024)
- [MoDi: Unconditional Motion Synthesis from Diverse Data](https://mlsanity.com/paper/2206.08010/) (2022)
- [Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation](https://mlsanity.com/paper/2305.09662/) (2023)
- [ReGenNet: Towards Human Action-Reaction Synthesis](https://mlsanity.com/paper/2403.11882/) (2024)
- [GENMO: A GENeralist Model for Human MOtion](https://mlsanity.com/paper/2505.01425/) (2025)
- [THOR: Text to Human-Object Interaction Diffusion via Relation Intervention](https://mlsanity.com/paper/2403.11208/) (2024)
- [Iterative Motion Editing with Natural Language](https://mlsanity.com/paper/2312.11538/) (2023)
- [Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance](https://mlsanity.com/paper/2401.15687/) (2024)
- [Motion Flow Matching for Human Motion Synthesis and Editing](https://mlsanity.com/paper/2312.08895/) (2023)
- [Interactive Humanoid: Online Full-Body Motion Reaction Synthesis with Social Affordance Canonicalization and Forecasting](https://mlsanity.com/paper/2312.08983/) (2023)
- [GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation](https://mlsanity.com/paper/2401.02142/) (2024)
- [Scaling Large Motion Models with Million-Level Human Motions](https://mlsanity.com/paper/2410.03311/) (2024)
- [Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation](https://mlsanity.com/paper/2502.05534/) (2025)
- [Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Sampling](https://mlsanity.com/paper/2308.01850/) (2023)
- [Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2301.03949/) (2023)
- [Language-guided Human Motion Synthesis with Atomic Actions](https://mlsanity.com/paper/2308.09611/) (2023)
- [HERO: Human Reaction Generation from Videos](https://mlsanity.com/paper/2503.08270/) (2025)
- [MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations](https://mlsanity.com/paper/2410.13790/) (2024)

## Abstract (arXiv)

We propose an action-conditional human motion generation method using variational implicit neural representations (INR). The variational formalism enables action-conditional distributions of INRs, from which one can easily sample representations to generate novel human motion sequences. Our method offers variable-length sequence generation by construction because a part of INR is optimized for a whole sequence of arbitrary length with temporal embeddings. In contrast, previous works reported difficulties with modeling variable-length sequences. We confirm that our method with a Transformer decoder outperforms all relevant methods on HumanAct12, NTU-RGBD, and UESTC datasets in terms of realism and diversity of generated motions. Surprisingly, even our method with an MLP decoder consistently outperforms the state-of-the-art Transformer-based auto-encoder. In particular, we show that variable-length motions generated by our method are better than fixed-length motions generated by the state-of-the-art method in terms of realism and diversity. Code at https://github.com/PACerv/ImplicitMotion.