Palette2Interior Architecture: From Syntactic and Semantic Colour Palettes to Generative Interiors with Deep Learning (original) (raw)
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Architecture & Style | A New Frontier for AI in Architecture
Harvard University, 2019
We build here upon a previous piece, where our emphasis revolved around the strict organization of floor plans and their generation, using Artificial intelligence, and more specifically Generative Adversarial Neural Networks (GANs). As we refine our ability to generate floor plans, we raise the question of the bias intrinsic to our models and offer here to extend our study beyond the simple imperative of organization. We investigate architectural style learning, by training and tuning an array of models on specific styles: Baroque, Row House, Victorian Suburban House, & Manhattan Unit. Beyond the simple gimmick of each style, our study reveals the deeper meaning of stylistic: more than its mere cultural significance, style carries a fundamental set of functional rules that defines a clear mechanic of space and controls the internal organization of the plan. In this new article, we will try to evidence the profound impact of architectural style on the composition of floor plans. ------------------------------------------------------------------------------------------------------------------------------------ Article: https://towardsdatascience.com/architecture-style-ded3a2c3998f
2022
In recent years, generative models have been rapidly transforming into a broad field of research, and artificial intelligence (AI) works are increasing. Since deep learning technologies such as Generative Adversarial Networks (GANs) providing synthesized new images are becoming more accessible, researchers in the design and related fields are very much interested in adapting GANs into practice. Especially, StyleGAN has a strong capability for image learning, reconstruction simulation, and absorbing the pixel characteristics of images in the input dataset. StyleGAN also produces similar imitation outputs and summarizes all the input data into one "average output". The study aims to reveal the potential of these outputs that can be employed as a visual inspiration aid for designers. This article will discuss the outputs of the experiments, findings, and prospects of StyleGAN.
International Congress on Human-Computer Interaction, Optimization and Robotic Applications”, 2023
We live in the age of Artificial Intelligence (AI) which permeates all aspects of our lives, from spam filtering to image classification on social media. While it is already wellestablished in industries ranging from heavy manufacturing to the IT field, its impact on the design professions remains relatively unexplored. This essay explores the use of neural networks in architecture, which is arguably the first genuinely 21st-century design technique and discusses experiments with Generative Adversarial Networks (GANs) to generate unexplored futuristic possible noble forms in architecture. In this way this paper also raises the question if machine can generate noble forms through its creative data optimization process. In this process one of the most famous heritages building of Bangladesh 60 dome mosque (Shat Gombuj Moshjid) has been examined to get expected result. Furthermore, this paper discusses how AI can be used as a personalized tool for architects to generate and express design ideas. It evaluates popular datasets for architectural purposes and considers the potential outcomes of experiments. The input of AI in the design process could usher in a new era of architectural design. As data continues to grow, it is shaping our collective future. Therefore, this paper concludes that it is essential to prepare our trained datasets to accept the future which might open up an extraordinary new chapter in the architectural realm.
Color-Patterns to Architecture Conversion through Conditional Generative Adversarial Networks
Biomimetics
Often an apparent complex reality can be extrapolated into certain patterns that in turn are evidenced in natural behaviors (whether biological, chemical or physical). The Architecture Design field has manifested these patterns as a conscious (inspired designs) or unconscious manner (emerging organizations). If such patterns exist and can be recognized, can we therefore use them as genotypic DNA? Can we be capable of generating a phenotypic architecture that is manifestly more complex than the original pattern? Recent developments in the field of Evo-Devo around gene regulators patterns or the explosive development of Machine Learning tools could be combined to set the basis for developing new, disruptive workflows for both design and analysis. This study will test the feasibility of using conditional Generative Adversarial Networks (cGANs) as a tool for coding architecture into color pattern-based images and translating them into 2D architectural representations. A series of scaled...
Apartment Floor Plans Generation via Generative Adversarial Networks
Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA), 2020
When drawing architectural plans, designers should always define every detail, so the images can contain enough information to support design. This process usually costs much time in the early design stage when the design boundary has not been finally determined. Thus the designers spend a lot of time working forward and backward drawing sketches for different site conditions. Meanwhile, Machine Learning, as a decision-making tool, has been widely used in many fields. Generative Adversarial Network (GAN) is a model frame in machine learning, specially designed to learn and generate image data. Therefore, this research aims to apply GAN in creating architectural plan drawings, helping designers automatically generate the predicted details of apartment floor plans with given boundaries. Through the machine learning of image pairs that show the boundary and the details of plan drawings, the learning program will build a model to learn the connections between two given images, and then the evaluation program will generate architectural drawings according to the inputted boundary images. This automatic design tool can help release the heavy load of architects in the early design stage, quickly providing a preview of design solutions for architectural plans.
DesIGN: Design Inspiration from Generative Networks
Computer Vision – ECCV 2018 Workshops, 2019
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include: (i) different Generative Adversarial Networks architectures that start from noise vectors to generate fashion items, (ii) novel loss functions that encourage creativity, inspired from Sharma-Mittal divergence, a generalized mutual information measure for the widely used relative entropies such as Kullback-Leibler, and (iii) a generation process following the key elements of fashion design (disentangling shape and texture components). A key challenge of this study is the evaluation of generated designs and the retrieval of best ones, hence we put together an evaluation protocol associating automatic metrics and human experimental studies that we hope will help ease future research. We show that our proposed creativity losses yield better overall appreciation than the one employed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelty and likability.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Aesthetic appeal is a primary driver of customer consideration for products such as automobiles. Product designers must accordingly convey design a ributes (e.g., 'Sportiness'), a challenging proposition given the subjective nature of aesthetics and heterogeneous market segments with potentially di erent aesthetic preferences. We introduce a scalable deep learning approach that predicts how customers across di erent market segments perceive aesthetic designs and provides a visualization that can aid in product design. We tested this approach using a large-scale product design and crowdsourced customer data set with a Siamese neural network architecture containing a pair of conditional generative adversarial networks. e results show that the model predicts aesthetic design a ributes of customers in heterogeneous market segments and provides a visualization of these aesthetic perceptions. is suggests that the proposed deep learning approach provides a scalable method for understanding customer aesthetic perceptions.
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Figure 1. The paper makes a breakthrough in the task of automated house layout generation. The right shows the mix of a ground-truth design made by an architect and our generated samples, based on the input bubble-diagram. Can you tell which one is the ground-truth? See the end of the caption for the answer. The paper proposes a novel generative adversarial layout refinement network, whose generator is trained to repeatedly apply and refine the design towards perfection. (The second sample from the right is the ground-truth.
Understanding and Visualizing Generative Adversarial Networks in Architectural Drawings
Learning, Prototyping and Adapting, Short Paper Proceedings of the 23rd International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA), 2018
Generative Adversarial Network (GAN) is a model framework in machine learning. It's specially used for learning and generating input and output data with similar or same format. PIX2PIXHD is a refined version of GAN, it's designed for learning image data in pairs, and generating predicted images based on the network model. The author applied PIX2PIXHD to learning and generating architectural drawings, marking rooms with different colours automatically by computer programs. Then, to understand how this network works, the author analysed the frame of the network, and give a detailed explanation about the three working principles of this network, convolution layer, residual network layer and deconvolution layer. Last, to visualize the network in architectural drawings, the author exported the data from each layer and each training epoch as grayscale images, finding that the features of architectural plan drawings have been learned step by step, and stored in the network as parameters. And the features in the drawings become more concise as the network goes deeper, and clearer as the training epoch increases. It might be inspiring comparing to the learning process of our human beings.