Evolving Evocative 2D Views of Generated 3D Objects
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 8 Nov 2021.
Builds on
6 of 8 references.
References · 6
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
- 2019Searching for MobileNetV3cited by 1,984
- 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
- 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 267
- 2019Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesiscited by 104
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 1.
2 papers.