Summary of Di-pcg: Diffusion-based Efficient Inverse Procedural Content Generation For High-quality 3d Asset Creation, by Wang Zhao et al.
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation
by Wang Zhao, Yan-Pei Cao, Jiale Xu, Yuejiang Dong, Ying Shan
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel method, called DI-PCG, is proposed for Inverse Procedural Content Generation (IPCG) from general image conditions. The lightweight diffusion transformer model at its core treats PCG parameters as the denoising target and observed images as conditions to control parameter generation. This efficient approach demonstrates superior performance in recovering parameters accurately and generalizing well to in-the-wild images, with only 7.6M network parameters and 30 GPU hours for training. DI-PCG is validated through quantitative and qualitative experiment results, showcasing its effectiveness in inverse PCG and image-to-3D generation tasks. This method offers a promising approach for efficient IPCG and represents an exploration step towards constructing 3D assets using parametric models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine creating detailed 3D shapes from scratch. It’s hard to control how they look, requiring lots of tweaking. Researchers have been trying to make it easier by developing new methods. One method, called DI-PCG, is super efficient and good at finding the right parameters to create desired shapes. All you need is a picture, and DI-PCG can generate the 3D shape from that image. This breakthrough could lead to creating many more realistic and detailed 3D assets. |
Keywords
» Artificial intelligence » Diffusion » Transformer
