# Read My Mind: A Multi-Modal Dataset for Human Belief Prediction

arXiv: https://arxiv.org/abs/2304.14501 · HTML: https://mlsanity.com/paper/2304.14501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [A Study of Comfortability between Interactive AI and Human](https://mlsanity.com/paper/2302.14360/) (2023)

## Built on by (most cited first, 1 of 1)

- [HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer](https://mlsanity.com/paper/2305.12369/) (2023)

## Abstract (arXiv)

Understanding human intentions is key to enabling effective and efficient human-robot interaction (HRI) in collaborative settings. To enable developments and evaluation of the ability of artificial intelligence (AI) systems to infer human beliefs, we introduce a large-scale multi-modal video dataset for intent prediction based on object-context relations.