A step towards procedural terrain generation with GANs

程序化地形生成与风格迁移

Procedural terrain generation with style transfer

本研究提出一种结合程序化噪声生成与增强神经风格迁移的地形生成技术，以低计算成本生成多样且贴近真实形态的地形，并优于传统程序化方法。

地形扩散：一种基于扩散的继任者，用于无限实时地形生成

Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise in Infinite, Real-Time Terrain Generation

Terrain Diffusion通过结合扩散模型的保真度与程序化噪声的特性，实现了高效且逼真的无限实时地形生成。

DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art

Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for digital art is growing aggressively. In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training. We compare various architectures and designs of DC-GANs to …

MESA：使用潜在扩散和全球哥白尼数据的文本驱动地形生成

MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data

针对传统地形建模依赖过程式技术需专业知识和手工规则的问题，提出MESA数据中心方案，在全球遥感数据上训练扩散模型，从文本生成高质量地形样本，公开Major TOM Core-DEM扩展数据集，为地形生成提供灵活可扩展的强大工具。

用于自主不平坦地形导航的自适应扩散地形生成器

Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation

提出自适应扩散地形生成器（ADTG），利用去噪扩散概率模型动态生成适应策略的复杂地形，显著提升机器人不平坦地形导航性能。

Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart

Recent advances in deep generative models have shown promising potential in image inpanting, which refers to the task of predicting missing pixel values of an incomplete image using the known context. However, existing methods can be slow or generate unsatisfying results with easily detectable flaws. In addition, there is often perceivable discontinuity near the holes and require further post-processing to blend the results. We present a new approach to address the difficulty of training a very deep generative model to synthesize high-quality photo-realistic inpainting. Our model uses conditio…