Generative Deep Learning for Visual Animation in Landscapes Design

Abstract

The biggest challenge for architecture designers is the time required for the design process. Especially landscape architects who have different work limits from architects in general. In contrast to architects in general, who are assisted in producing design plans by building standards, building requirements, and space programs that adapt to the type of project being undertaken. At the same time, some design jobs demand high-productivity landscape animation presentation in a short time. The long process involved in designing animation often makes it difficult for designers to produce optimal work. This study proposes generative zooming animation with artificial intelligence support to shorten the designer’s work process and energy optimization. Deep learning with Vector Quantized Generative Adversarial Network and Contrastive Language-Image Pre-Training was used to generate alternative landscape designs from text prompt-based and compile them in animation. Our experiment shows that one frame can be generated roughly in 3.636 ± 0.089 s, which is significantly faster than the conventional method to create animation. Moreover, our method is able to achieve a good-quality image, which scored 3.2904 using inception score evaluation. The effectiveness of deep learning in visual landscape and animation creation can help designers speed up the design process. Furthermore, working time efficiency without compromising design quality will increase designer productivity and economic growth.