Procedural Content Generation (PCG) is powerful in creating high-quality 3D contents, yet controlling it to produce desired shapes is difficult and often requires extensive parameter tuning. Inverse Procedural Content Generation aims to automatically find the best parameters under the input condition. However, existing sampling-based and neural network-based methods still suffer from numerous sample iterations or limited controllability. In this work, we present DI-PCG, a novel and efficient method for Inverse PCG from general image conditions. At its core is a lightweight diffusion transformer model, where PCG parameters are directly treated as the denoising target and the observed images as conditions to control parameter generation. DI-PCG is efficient and effective. With only 7.6M network parameters and 30 GPU hours to train, it demonstrates superior performance in recovering parameters accurately, and generalizing well to in-the-wild images. Quantitative and qualitative experiment results validate the effectiveness of DI-PCG in inverse PCG and image-to-3D generation tasks. DI-PCG offers a promising approach for efficient inverse PCG and represents a valuable exploration step towards a 3D generation path that models how to construct a 3D asset using parametric models.

本研究解决了现有程序内容生成（PCG）方法在控制目标形状方面面临的挑战，提出了一种新的高效逆PCG方法DI-PCG。通过利用轻量级扩散变换模型，该方法能够从一般图像条件下直接生成PCG参数，实现高效且精确的参数恢复，展示了在逆PCG和图像到3D生成任务中的优越性能。

DI-PCG：基于扩散的高效逆程序内容生成用于高质量3D资产创建