# Visual Chirality

arXiv: https://arxiv.org/abs/2006.09512 · HTML: https://mlsanity.com/paper/2006.09512/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Doppelgangers: Learning to Disambiguate Images of Similar Structures](https://mlsanity.com/paper/2309.02420/) (2023)
- [Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations](https://mlsanity.com/paper/2110.04383/) (2021)
- [KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models](https://mlsanity.com/paper/2407.17773/) (2024)
- [Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening](https://mlsanity.com/paper/2509.08502/) (2025)
- [Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models](https://mlsanity.com/paper/2310.08577/) (2023)
- [Substance or Style: What Does Your Image Embedding Know?](https://mlsanity.com/paper/2307.05610/) (2023)
- [3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval](https://mlsanity.com/paper/2211.05352/) (2022)
- [RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation](https://mlsanity.com/paper/2508.13968/) (2025)
- [Symmetry Understanding of 3D Shapes via Chirality Disentanglement](https://mlsanity.com/paper/2508.05505/) (2025)
- [HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars](https://mlsanity.com/paper/2203.14863/) (2022)
- [Anomaly Detection with Test Time Augmentation and Consistency Evaluation](https://mlsanity.com/paper/2206.02345/) (2022)
- [Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network](https://mlsanity.com/paper/2211.06641/) (2022)
- [Feature Chirality in Deep Learning Models](https://mlsanity.com/paper/2305.03966/) (2023)
- [Can Motion-Language Models Ground Structure? STRIDE for Evaluating the Evaluators](https://mlsanity.com/paper/2609.32462/) (2026)

## Abstract (arXiv)

How can we tell whether an image has been mirrored? While we understand the geometry of mirror reflections very well, less has been said about how it affects distributions of imagery at scale, despite widespread use for data augmentation in computer vision. In this paper, we investigate how the statistics of visual data are changed by reflection. We refer to these changes as "visual chirality", after the concept of geometric chirality - the notion of objects that are distinct from their mirror image. Our analysis of visual chirality reveals surprising results, including low-level chiral signals pervading imagery stemming from image processing in cameras, to the ability to discover visual chirality in images of people and faces. Our work has implications for data augmentation, self-supervised learning, and image forensics.