Live review | Reshape the interior design workflow with AI!

AIGC applications are profoundly changing our work and life, and interior design is also deeply affected. Through innovative AI technologies, we are able to reimagine and design our interior spaces like never before, opening up entirely new possibilities for the living experience.

In this issue of Unbounded Talk, Jason, the manager of "Designer's Toolbox", AIGC creator, algorithm designer, and architectural designer, was invited to let him show us the application and mystery of AI in the field of interior design!

The following is the exciting content shared in this field-

Key Points - Summary

AI-assisted interior design, the ideal state is that AI can help designers complete the design process from 0 to 1. For example, a simple wall layout drawing can directly generate a three-dimensional drawing with various information such as construction and home decoration for designers through AI tools, forming an information model.

But the actual implementation will be more complicated. Taking the input terminal in the early stage as an example, in addition to the basic wall layout and space layout, there are also things like the owner's preferences, project budget, and even specific ground paving, hard decoration materials, soft decoration options, etc., each of which is different. type of output. How to better combine these AI tools with input and output?

According to the technical level, the interior design can be divided into 3 different stages:

1. Consulting stage: This is a stage where the commissioning and undertaking of the plan has not been finalized. Using large language models such as ChatGPT will be very good.

2. Conceptual Design: Customers hope to see as many customized solutions in different styles as possible at this stage that meet their personal preferences. Use AI drawing tools such as Stable Diffusion to assist designers and maximize work efficiency.

3. Conceptual modeling: For a conversion from image to 3D model, based on the unique scene of interior design, a 3D model can be generated through a simple plane sketch or layout drawing, but it has not been realized yet AI solution for fine modeling.

4. Design deepening: The current mainstream AI tools cannot assist designers very well, and traditional CAD or BIM models can be selected.

The Jason team tried to make several small tools, mainly focusing on the early stage of design consultation, concept design and concept model, and made some attempts in the later stage of design development.

In the consulting stage, for design companies, the large language model represented by ChatGPT has strong generalization. But when implemented at the application level, companies will have their own databases and corresponding needs, which can be organized into a vector library, and at the same time cooperate with AI agents to build an internal marketing or data retrieval platform.

Designers can also use such small tools to better organize their own resources, convert it into a vector knowledge base, and then use AI, a more efficient retrieval method, to improve the time cost of the information retrieval stage.

Jason shared some application technology routes based on the extension of the large language model: prompt word engineering, enterprise knowledge base, AI agent, large model fine-tuning and other four aspects:

**(1) Prompt word project: **The construction cost is relatively low. Based on the large model, it can be constrained by inputting prompt words, so that this model can be used as an auxiliary design tool to assist the designer to complete some work. But at present, it may be more suitable for some less rigorous and divergent scenarios, such as writing novels, or doing some simple analysis.

(2) Enterprise Knowledge Base: The cost will be slightly higher, and some professional R&D teams are required. It is necessary to have a certain understanding of the internal knowledge structure of the enterprise, and be able to efficiently convert them into a vector knowledge base for easy retrieval. It is very suitable for the application scenario of sales.

(3) AI agent: A simple understanding is an agent, or an agent, each agent will do one thing he is good at. Through the set rules and constraints, these different agents can be combined to form a complete workflow. Compared with the former two, it requires R&D personnel who understand business scenarios very well.

(4) Large model fine-tuning: The direction with the highest cost, and the cost of computing power is the bulk of it. For companies that are not in the direction of AI or the Internet, it is not suitable for fine-tuning in this direction, and you can consider cooperating with corresponding companies.

There is a pain point in the profession of designers - there are a lot of specifications and data that need to be memorized.

Faced with this situation, designers can use AI to build a platform that can be used on the web or even on mobile phones, and input design data accumulated within the company or personally, such as "What are the commonly used sizes for restaurants?" and so on. The AI model can combine the input knowledge base to output a very accurate answer. For some design specifications, AI can also give designers a more accurate reminder.

Because large models are trained on previously informative data, their strength lies in generalization. But for internal company documents, documents that have not even been released to the Internet, these AI models cannot be accurately consulted. Therefore, it is necessary to use the unique knowledge base of enterprises or individuals for training based on these large models in order to implement the application scenarios of information retrieval.

This extends a new application scenario, AI agent, which has a very promising prospect.

For example, in an e-commerce scenario, it can realize question-and-answer dialogues with customers in the form of sales, and can also search through different tools in the background to help users retrieve information, assist in generating floor plans, images, and even rendering models.

In fact, generating floor plans, generating images, and generating rendering models are three different AI agents, and each workflow is responsible for different tasks.

Based on the large model and the professional sorting out of industry business scenarios by enterprises and practitioners, these AI agents can be integrated and combined into an efficient system.

Users can input different information through this interface, and let AI recommend different schemes and furniture combinations, etc. Even the furniture combination given by AI can be set and selected from the input product library to truly help users or enterprises integrate their own business with AI in-depth scenarios.

Back to the interior design scene. The designer will first determine the design style, because the style itself is very diverse, and the needs of users are often changing. At the same time, the style itself will also affect the overall color composition of the interior, the choice of furniture and soft decoration, etc. If the designer does not allow users to determine the style in the early stage, the subsequent workload will be very heavy.

Therefore, when designing in the industry at present, a plane is determined first, and then a style is determined, which is more detailed. Therefore, various styles are trained first to create an AI model. When the user brings a floor plan, the designer uses the AI tool Assisted Rendering to quickly generate and switch styles.

This tool will help customers have a quick feedback and experience in the early stage, increase the work efficiency of enterprises and practitioners, and at the same time improve the user experience and help the company get business.

In addition, the e-commerce scene also has a need for publicity of furniture or decorations, or a need for reference pictures for customers or designers.

In this scenario, the pictures quickly generated using SD still need to be optimized before they can be used. It can be combined with product tonality, or even adjusted according to product target customers, to create a customized AI model to meet different scenarios and business needs.

In addition to assisting concept rendering, AI can also assist modeling. At this point, the design requires that the information be accurate and feasible, and many supply chains will be involved.

First of all, design a multi-modality model, which can be simply understood as a model that can be generated through text or voice, or a model that can be generated through pictures, images, videos, etc. Conversely, this model can also deduce these different forms of information.

AI image generation is a process from scratch. For example, using the concept design of multiple V-shaped flower pots, using open source algorithms, you can generate different rough models in the early stage. Its shape is relatively accurate and can reflect the Image object structure. At the same time, through some other traditional optimization algorithms, this relatively rough model can be converted into a 3D mesh that can be edited again, and even retopology.

In this way, the designer can generate several AI concept designs in 10 seconds, and then use this small tool to quickly convert them into an editable thing in 30 seconds, which will greatly save the time to start modeling from 0 , Improve the designer's work efficiency.

So, what can AI do in terms of deepening maps?

Perhaps the current open source technology has no way to efficiently combine with the later implementation of interior design. Because the drawing technology that AI is currently good at is still based on 2D space. The real design scene has very high requirements on the scale and accuracy of the overall space, as well as many details.

"I believe that with the maturity of 3D large models and multi-modality, this efficiency will gradually be built, and it will become more and more mature." Jason said that at present, it still needs to be combined with traditional technology to assist in the completion Deepen the drawing.

The first application scenario is somewhat similar to the AI marketing scenario shown at the beginning, combined according to the company's internal furniture materials and products. For example, AI can be used to produce cost images, but there will be corresponding product links in this image. This can be regarded as using AI images to convert the information contained in it into product information.

The second scene is about material migration. Interior design can be simply understood as being composed of visual layer, geometric layer and even other finer layers. The advantage of AI is that it can quickly generate an overall space with a sense of atmosphere. Although this kind of light and shadow may be inaccurate, it can still help us quickly find the color scheme and space composition.

If the information contained in the image generated by AI can be extracted and transformed into our model, it can speed up the modeling process of the designer.

Q&A - Review

Ruiya: What do you think will be the first field where AI will land in the interior design work? Is it design creative rendering or something else?

Jason: It must be the design creativity, and it is more concentrated in the early stage. Now we are talking about cost reduction and efficiency increase, which is a matter that can increase the efficiency potential of enterprises to a certain extent. Whether it is based on online marketing or SD-based quick drawing, it can upgrade the customer experience of customers. There is also a greater chance of attracting more customers.

But rendering may not become the mainstream in the future, because AI or SD images are inaccurate in terms of indoor light and shadow generated. Based on these deviations, there will be greater deviations in the landing effect.

**Ruiya: Have you considered making a tool that allows C-end users to make their own design drawings in a very simple way? **

Jason: It is actually quite difficult to make such a product. In particular, there are companies in the industry that have accumulated over the years, such as Kujiale and Sanweijia. They use AI technology to make it easier to make better products in this area. I think small teams have almost no chance in this regard.

But when it comes to this, such products in the future will definitely become simpler and simpler, and the role of designers in the concept design stage will be greatly reduced. I think that the core value of designers may need to be transferred in the future. It is more about how to better implement the concept map that the owner likes.

**Ruiya: In your current observations, are there any examples of designs using AI that have been implemented? **

Jason: If it is completely from concept to completion, I haven't seen this one yet. But if it is an AI-assisted concept rendering, the owner can determine the style through the concept drawing and other applications.

**Ruiya: Do you think that AI is easily affected by the bias of the training set when performing assisted design, resulting in a lack of diversity in design results? **

Jason: There must be such a situation. Although we are adding our own data set training model on the basis of the large model. But these training maps themselves may also be maps that can be found on the Internet, and other alchemists can also find them.

However, the problem of homogenization may also be affected by trends or trend factors from a design perspective. Under a certain trend, image materials on the entire Internet may be biased towards this style.

**Ruiya: How can designers still have the ability to control and adjust the final result during AI rendering? **

Jason: For a designer, it's not that hard for you to control the outcome. I think the difficulty lies in whether the plan that the designer thinks can meet the needs of the owner.

**Rui Ya: Do you think AI will change our understanding of design aesthetics? **

Jason: Definitely. With the popularity of AI tools, everyone's aesthetics will actually be raised to a higher level. Therefore, under this trend, designers must improve their own aesthetics. At the same time, this will also increase the innovation requirements of designers. One source of innovation is that everyone needs to change the traditional way of looking for reference pictures to do design. So I think future designers, or those who want to be high-end designers, will have to change this way.

**Ruiya: Through the continuous iteration of the training set, do you think that in the future, will AI have the ability to perceive and predict the future needs and trends of users? **

Jason: Yes. What AI is good at is processing a large amount of data to summarize and even make some predictions.

**Ruiya: Until now, there are still a large number of traditional interior designers who are worried that AI may replace their jobs. In this situation, how would you argue to them that designers are irreplaceable in terms of creativity and human touch? **

Jason: The problem of AI replacing traditional designers is actually the pursuit of reducing costs and increasing efficiency in the entire economic society. The AI revolution is actually replacing some of the most repetitive and non-creative work.

Creative designers can never be replaced by tools. If you are a more technical interior designer, it is recommended to embrace AI technology and learn more about its underlying principles, because no layman can do industry model training or packaging of high-quality data sets.

In addition, designers who are good at communication can actually strengthen their own abilities. Of course, you may need to change your thinking, that is, how to use these tools to help you find more customers better, or build your personal IP or brand.

Various industries are talking about reducing costs and increasing efficiency. At present, it is not difficult to achieve cost reduction with the assistance of AI. But if AI tools are used to achieve better efficiency, it is another long-term topic, and many new opportunities will also arise from it.