# Semantic Compression of 3D Objects for Open and Collaborative Virtual Worlds

arXiv: https://arxiv.org/abs/2505.16679 · HTML: https://mlsanity.com/paper/2505.16679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [OLMo: Accelerating the Science of Language Models](https://mlsanity.com/paper/2402.00838/) (2024)
- [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://mlsanity.com/paper/2306.16928/) (2023)
- [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](https://mlsanity.com/paper/2310.15110/) (2023)
- [Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors](https://mlsanity.com/paper/2306.17843/) (2023)
- [Text + Sketch: Image Compression at Ultra Low Rates](https://mlsanity.com/paper/2307.01944/) (2023)
- [K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences](https://mlsanity.com/paper/2408.14468/) (2024)
- [Towards improved lossy image compression: Human image reconstruction with public-domain images](https://mlsanity.com/paper/1810.11137/) (2018)
- [Exploring the Limits of Semantic Image Compression at Micro-bits per Pixel](https://mlsanity.com/paper/2402.13536/) (2024)

## Abstract (arXiv)

Traditional methods for 3D object compression operate only on structural information within the object vertices, polygons, and textures. These methods are effective at compression rates up to 10x for standard object sizes but quickly deteriorate at higher compression rates with texture artifacts, low-polygon counts, and mesh gaps. In contrast, semantic compression ignores structural information and operates directly on the core concepts to push to extreme levels of compression. In addition, it uses natural language as its storage format, which makes it natively human-readable and a natural fit for emerging applications built around large-scale, collaborative projects within augmented and virtual reality. It deprioritizes structural information like location, size, and orientation and predicts the missing information with state-of-the-art deep generative models. In this work, we construct a pipeline for 3D semantic compression from public generative models and explore the quality-compression frontier for 3D object compression. We apply this pipeline to achieve rates as high as 105x for 3D objects taken from the Objaverse dataset and show that semantic compression can outperform traditional methods in the important quality-preserving region around 100x compression.