# Shape complexity estimation using VAE

arXiv: https://arxiv.org/abs/2304.02766 · HTML: https://mlsanity.com/paper/2304.02766/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 5 Apr 2023.

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

- [Critic Guided Segmentation of Rewarding Objects in First-Person Views](https://mlsanity.com/paper/2107.09540/) (2021)
- [Behavioral Cloning via Search in Video PreTraining Latent Space](https://mlsanity.com/paper/2212.13326/) (2022)
- [Transfer Learning with Jukebox for Music Source Separation](https://mlsanity.com/paper/2111.14200/) (2021)
- [Stroke-based Rendering: From Heuristics to Deep Learning](https://mlsanity.com/paper/2302.00595/) (2022)

## Built on by (most cited first, 4 of 4)

- [Benchmarks for Physical Reasoning AI](https://mlsanity.com/paper/2312.10728/) (2023)
- [Digital Twin Generation from Visual Data: A Survey](https://mlsanity.com/paper/2504.13159/) (2025)
- [Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks](https://mlsanity.com/paper/2312.05925/) (2023)
- [Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs](https://mlsanity.com/paper/2403.13801/) (2024)

## Abstract (arXiv)

In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with different sizes of latent vectors. Although complexity of a shape is not a well defined attribute, different aspects of it can be estimated. We demonstrate that our methods captures some aspects of shape complexity. Code and training details will be publicly available.