# Evolving Evocative 2D Views of Generated 3D Objects

arXiv: https://arxiv.org/abs/2111.04839 · HTML: https://mlsanity.com/paper/2111.04839/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://mlsanity.com/paper/2002.10880/) (2020)
- [Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis](https://mlsanity.com/paper/1912.05237/) (2019)

## Built on by (most cited first, 2 of 2)

- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [Evolving Three Dimension (3D) Abstract Art: Fitting Concepts by Language](https://mlsanity.com/paper/2304.12932/) (2023)

## Abstract (arXiv)

We present a method for jointly generating 3D models of objects and 2D renders at different viewing angles, with the process guided by ImageNet and CLIP -based models. Our results indicate that it can generate anamorphic objects, with renders that both evoke the target caption and look visually appealing.