# AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction

arXiv: https://arxiv.org/abs/2005.08307 · HTML: https://mlsanity.com/paper/2005.08307/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2020.

## Built on by (most cited first, 16 of 16)

- [Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2206.05712/) (2022)
- [How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting](https://mlsanity.com/paper/2203.04781/) (2022)
- [Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction](https://mlsanity.com/paper/2204.11561/) (2022)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports](https://mlsanity.com/paper/2410.17785/) (2024)
- [EgoTraj-Bench: Towards Robust Trajectory Prediction Under Ego-view Noisy Observations](https://mlsanity.com/paper/2510.00405/) (2025)
- [T2FPV: Dataset and Method for Correcting First-Person View Errors in Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2209.11294/) (2022)
- [Distilling Knowledge for Short-to-Long Term Trajectory Prediction](https://mlsanity.com/paper/2305.08553/) (2023)
- [FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputation](https://mlsanity.com/paper/2410.15248/) (2024)
- [GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2311.14922/) (2023)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Learning a Pedestrian Social Behavior Dictionary](https://mlsanity.com/paper/2212.01426/) (2022)
- [GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2312.04479/) (2023)
- [CrowdMAC: Masked Crowd Density Completion for Robust Crowd Density Forecasting](https://mlsanity.com/paper/2407.14725/) (2024)
- [Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines](https://mlsanity.com/paper/2402.03457/) (2024)

## Abstract (arXiv)

Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A key component of this task is represented by the inherently multi-modal nature of human paths which makes socially acceptable multiple futures when human interactions are involved. To this end, we propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs). Conditioning mainly relies on prior belief maps, representing most likely moving directions and forcing the model to consider past observed dynamics in generating future positions. Human interactions are modeled with a graph-based attention mechanism enabling an online attentive hidden state refinement of the recurrent estimation. To corroborate our model, we perform extensive experiments on publicly-available datasets (e.g., ETH/UCY, Stanford Drone Dataset, STATS SportVU NBA, Intersection Drone Dataset and TrajNet++) and demonstrate its effectiveness in crowded scenes compared to several state-of-the-art methods.