Texturing & modeling : a procedural approach : 日本語版

Procedural generation of 3D karst caves with speleothems

In this article, we survey the state-of-the-art in procedural generation of buildings (PGB). We analyzed the design choices, capabilities, and limitations of 71 PGB approaches. We structured our analysis based on a taxonomy of procedural content generation (PCG) that we adapted to PGB by adding relevant details. Overall, we identified 13 taxonomical aspects, which we organized in three groups: 1) generative capabilities, 2) accessibility aspects, and 3) technological aspects of the implementation. It is shown that the generation of different parts of buildings is easiest to accomplish with different PGB techniques. While some approaches built on other ones or even integrate two or more of them to arrive at solutions with a greater effective generative scope, all of them still exhibit rather basic limitations. For instance, the choice of room geometries might be limited to rectangular rooms, they might require numerous manual creation steps, or they might yield potentially invalid output. In addition, only few approaches implement fitting interiors and exteriors, which can be used as complete, traversable buildings in virtual environments. Hence, the combination and therefore integration of different approaches for different parts of buildings is a major concern of procedural building generators, rendering their compatibility a characteristic of central importance.

Recent Advances in Procedural Generation of Buildings: From Diversity to Integration

Image generation is the task of automatically generating an image using an input vector z. In recent years, the quest to understand and manipulate this input vector has gained more and more attention due to potential applications. The previous works have shown promising results in interpreting the latent space of pre-trained Generator G to generate images up to 256 x 256 using supervised and unsupervised techniques. This paper addresses the challenge of interpreting the latent space of pre-trained Generator G to generate high-resolution images, i.e., images with resolution up to 1024x1024. This problem is tackled by proposing a new framework that iterates upon Cyclic Reverse Generator (CRG) by upgrading Encoder E present in CRG to handle high-resolution images. This model can successfully interpret the latent space of the generator in complex generative models like Progressive Growling Generative Adversarial Network (PGGAN) and StyleGAN. The framework then maps input vector zf with image attributes defined in the dataset. Moreover, it gives precise control over the output of generator models. This control over generator output is tremendously helpful in enhancing computer vision applications like photo editing and face manipulation. One downside of this framework is the reliance on a comprehensive dataset, thus limiting the use of it.

GAN Inversion of High-Resolution Images

The recent success of pre-trained diffusion models unlocks the possibility of the automatic generation of textures for arbitrary 3D meshes in the wild. However, these models are trained in the screen space, while converting them to a multi-view consistent texture image poses a major obstacle to the output quality. In this paper, we propose a novel method to enforce multi-view consistency. Our method is based on the observation that latent space in a pre-trained diffusion model is noised separately for each camera view, making it difficult to achieve multi-view consistency by directly manipulating the latent codes. Based on the celebrated Denoising Diffusion Implicit Models (DDIM) scheme, we propose to use an optimization-based color-fusion to enforce consistency and indirectly modify the latent codes by gradient back-propagation. Our method further relaxes the sequential dependency assumption among the camera views. By evaluating on a series of general 3D models, we find our simple approach improves consistency and overall quality of the generated textures as compared to competing state-of-the-arts. Our implementation is available at: https://github.com/Quantuman134/TexPainter

pdf/texpainter-generative-mesh-texturing-with-multi-view-1sap20q4lm.pdf

TexPainter: Generative Mesh Texturing with Multi-view Consistency

Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture generation is the process of creating textures using mathematical models. The input to these models can be a set of parameters, random values generated by noise functions, or existing texture images, which may be further processed or combined to generate new textures. Many methods for procedural texture generation have been proposed, but there has been no comprehensive survey or comparison of them yet. In this paper, we present a review of different procedural texture generation methods, according to the characteristics of the generated textures. We divide the different generation methods into two categories: structured texture and unstructured texture generation methods. Example textures are generated using these methods with varying parameter values. Furthermore, we survey post-processing methods based on the filtering and combination of different generation models. We also present a taxonomy of different models, according to the mathematical functions and texture samples they can produce. Finally, a psychophysical experiment is designed to identify the perceptual features of the example textures. Finally, an analysis of the results illustrates the strengths and weaknesses of these methods.

Survey of Procedural Methods for Two-Dimensional Texture Generation

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Survey of Procedural Methods for Two-Dimensional Texture Generation