# Forecasting Human Dynamics from Static Images

arXiv: https://arxiv.org/abs/1704.03432 · HTML: https://mlsanity.com/paper/1704.03432/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2017.

## Built on by (most cited first, 17 of 17)

- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild](https://mlsanity.com/paper/2104.04029/) (2021)
- [Imitation Learning for Human Pose Prediction](https://mlsanity.com/paper/1909.03449/) (2019)
- [Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors](https://mlsanity.com/paper/2302.04860/) (2023)
- [A generic diffusion-based approach for 3D human pose prediction in the wild](https://mlsanity.com/paper/2210.05669/) (2022)
- [Image-to-Video Generation via 3D Facial Dynamics](https://mlsanity.com/paper/2105.14678/) (2021)
- [Toward Reliable Human Pose Forecasting with Uncertainty](https://mlsanity.com/paper/2304.06707/) (2023)
- [What Happened 3 Seconds Ago? Inferring the Past with Thermal Imaging](https://mlsanity.com/paper/2304.13651/) (2023)
- [Social Processes: Self-Supervised Meta-Learning over Conversational Groups for Forecasting Nonverbal Social Cues](https://mlsanity.com/paper/2107.13576/) (2021)
- [Exploring Versatile Prior for Human Motion via Motion Frequency Guidance](https://mlsanity.com/paper/2111.13074/) (2021)
- [Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks](https://mlsanity.com/paper/2109.05776/) (2021)
- [mmPred: Radar-based Human Motion Prediction in the Dark](https://mlsanity.com/paper/2512.00345/) (2025)
- [Towards Consistent Long-Term Pose Generation](https://mlsanity.com/paper/2507.18382/) (2025)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [LARNet: Latent Action Representation for Human Action Synthesis](https://mlsanity.com/paper/2110.10899/) (2021)
- [Unified Simulation, Perception, and Generation of Human Behavior](https://mlsanity.com/paper/2204.13678/) (2022)
- [3D Pose Nowcasting: Forecast the Future to Improve the Present](https://mlsanity.com/paper/2308.12914/) (2023)

## Abstract (arXiv)

This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body poses in 3D. To address the problem, we propose the 3D Pose Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on single-image human pose estimation and sequence prediction, and converts the 2D predictions into 3D space. We train our 3D-PFNet using a three-step training strategy to leverage a diverse source of training data, including image and video based human pose datasets and 3D motion capture (MoCap) data. We demonstrate competitive performance of our 3D-PFNet on 2D pose forecasting and 3D pose recovery through quantitative and qualitative results.