# mmPred: Radar-based Human Motion Prediction in the Dark

arXiv: https://arxiv.org/abs/2512.00345 · HTML: https://mlsanity.com/paper/2512.00345/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Builds on (most cited first, 22 of 22)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [DLow: Diversifying Latent Flows for Diverse Human Motion Prediction](https://mlsanity.com/paper/2003.08386/) (2020)
- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic](https://mlsanity.com/paper/1805.02513/) (2018)
- [A generic diffusion-based approach for 3D human pose prediction in the wild](https://mlsanity.com/paper/2210.05669/) (2022)
- [Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space](https://mlsanity.com/paper/2207.07351/) (2022)
- [High Resolution Point Clouds from mmWave Radar](https://mlsanity.com/paper/2206.09273/) (2022)
- [Diffusion Model is a Good Pose Estimator from 3D RF-Vision](https://mlsanity.com/paper/2403.16198/) (2024)
- [2D Human Pose Estimation: A Survey](https://mlsanity.com/paper/2204.07370/) (2022)
- [CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion](https://mlsanity.com/paper/2305.12554/) (2023)
- [Fast and Scalable Human Pose Estimation using mmWave Point Cloud](https://mlsanity.com/paper/2205.00097/) (2022)
- [milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing](https://mlsanity.com/paper/2306.17010/) (2023)
- [A Survey of mmWave-based Human Sensing: Technology, Platform and Applications](https://mlsanity.com/paper/2308.03149/) (2023)
- [Forecasting Human Dynamics from Static Images](https://mlsanity.com/paper/1704.03432/) (2017)
- [3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning](https://mlsanity.com/paper/2108.02858/) (2021)
- [MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception](https://mlsanity.com/paper/2406.10708/) (2024)

## Built on by (most cited first, 3 of 3)

- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [A Two-Stage Motion-Aware Framework for mmWave-based Human Mesh Recovery](https://mlsanity.com/paper/2605.08530/) (2026)
- [RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals](https://mlsanity.com/paper/2608.00289/) (2026)

## Abstract (arXiv)

Existing Human Motion Prediction (HMP) methods based on RGB-D cameras are sensitive to lighting conditions and raise privacy concerns, limiting their real-world applications such as firefighting and healthcare. Motivated by the robustness and privacy-preserving nature of millimeter-wave (mmWave) radar, this work introduces radar as a novel sensing modality for HMP, for the first time. Nevertheless, radar signals often suffer from specular reflections and multipath effects, resulting in noisy and temporally inconsistent measurements, such as body-part miss-detection. To address these radar-specific artifacts, we propose mmPred, the first diffusion-based framework tailored for radar-based HMP. mmPred introduces a dual-domain historical motion representation to guide the generation process, combining a Time-domain Pose Refinement (TPR) branch for learning fine-grained details and a Frequency-domain Dominant Motion (FDM) branch for capturing global motion trends and suppressing frame-level inconsistency. Furthermore, we design a Global Skeleton-relational Transformer (GST) as the diffusion backbone to model global inter-joint cooperation, enabling corrupted joints to dynamically aggregate information from others. Extensive experiments show that mmPred achieves state-of-the-art performance, outperforming existing methods by 8.6% on mmBody and 22% on mm-Fi.