mlsanityData to 14 Oct 2026

OrthoPlanes: A Novel Representation for Better 3D-Awareness of GANs

arXiv 2309.15830 ↗First posted 27 Sep 2023cs.CV cs.AIcited by 6

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Abstract (arXiv)

We present a new method for generating realistic and view-consistent images with fine geometry from 2D image collections. Our method proposes a hybrid explicit-implicit representation called OrthoPlanes, which encodes fine-grained 3D information in feature maps that can be efficiently generated by modifying 2D StyleGANs. Compared to previous representations, our method has better scalability and expressiveness with clear and explicit information. As a result, our method can handle more challenging view-angles and synthesize articulated objects with high spatial degree of freedom. Experiments demonstrate that our method achieves state-of-the-art results on FFHQ and SHHQ datasets, both quantitatively and qualitatively. Project page: urlhttps://orthoplanes.github.io/.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 56 of its 73 references that are in the map. First posted on arXiv 27 Sep 2023.

Builds on

56 of 73 references.

References · 56

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38 more
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Cited by year

Citing papers per year: 2024: 5; 2025: 1.

6 papers.

  1. 2024CosmicMan: A Text-to-Image Foundation Model for Humanscited by 30
  2. 2024En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Datacited by 14
  3. 2025HyPlaneHead: Rethinking Tri-plane-like Representations in Full-Head Image Synthesiscited by 6
  4. 2024SYM3D: Learning Symmetric Triplanes for Better 3D-Awareness of GANscited by 5
  5. 2024SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generationcited by 3
  6. 2024NOVA-3D: Non-overlapped Views for 3D Anime Character Reconstructioncited by 2