Automatic generation of object shapes with desired functionalities
3D objects (artefacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities that it should provide, also known as functional requirements. Today, the design of 3D object models is still a slow and largely artisanal activity, with few Computer-Aided Design (CAD) tools existing to aid the exploration of the design solution space. The purpose of the study is to explore the possibility of shape generation conditioned on desired functionalities. To accelerate the design process, we introduce an algorithm for generating object shapes with desired functionalities. We follow the principle form follows function, and assume that the form of a structure is correlated to its function. First, we use an artificial neural network to learn a function-to-form mapping by analysing a dataset of objects labeled with their functionalities. Then, we combine forms providing one or more desired functions, generating an object shape that is expected to provide all of them. Finally, we verify in simulation whether the generated object possesses the desired functionalities, by defining and executing functionality tests on it.
 This paper proposed a 3D shape generation model. The model is essentially an auto-encoder. The authors explored a new way of interpolation among encoded latent vectors, and drew connections to object functionality. The paper is, unfortunately, clearly below the bar of ICLR in many ways. It’s technically incremental: the paper doesn’t propose a new model; it instead suggests new way of interpolating the latent vectors for shape generation. The incremental technical innovation is not well-motivated or justified, either: the definitions of new concepts such as ‘functional essence’ and ‘importance vector’ are ad-hoc. The results are poor, much worse compared with the state-of-the-art shape synthesis methods. The writing and organization can also be improved. For example, the main idea should be emphasized first in the method section, and the detailed network architecture can be saved for a separate subsection or supplementary material. It’s good that the authors are looking into the direction of modeling shape functionality. This is an importance area that is currently less explored. I suggest the authors look into the rich literature of geometry modeling in the computer graphics and vision community, and improve the paper by drawing inspiration from the latest progress there.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Automatic generation of object shapes with desired functionalities
3D objects (artefacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities that it should provide, also known as functional requirements. Today, the design of 3D object models is still a slow and largely artisanal activity, with few Computer-Aided Design (CAD) tools existing to aid the exploration of the design solution space. The purpose of the study is to explore the possibility of shape generation conditioned on desired functionalities. To accelerate the design process, we introduce an algorithm for generating object shapes with desired functionalities. We follow the principle form follows function, and assume that the form of a structure is correlated to its function. First, we use an artificial neural network to learn a function-to-form mapping by analysing a dataset of objects labeled with their functionalities. Then, we combine forms providing one or more desired functions, generating an object shape that is expected to provide all of them. Finally, we verify in simulation whether the generated object possesses the desired functionalities, by defining and executing functionality tests on it.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper proposed a 3D shape generation model. The model is essentially an auto-encoder. The authors explored a new way of interpolation among encoded latent vectors, and drew connections to object functionality. The paper is, unfortunately, clearly below the bar of ICLR in many ways. It’s technically incremental: the paper doesn’t propose a new model; it instead suggests new way of interpolating the latent vectors for shape generation. The incremental technical innovation is not well-motivated or justified, either: the definitions of new concepts such as ‘functional essence’ and ‘importance vector’ are ad-hoc. The results are poor, much worse compared with the state-of-the-art shape synthesis methods. The writing and organization can also be improved. For example, the main idea should be emphasized first in the method section, and the detailed network architecture can be saved for a separate subsection or supplementary material. It’s good that the authors are looking into the direction of modeling shape functionality. This is an importance area that is currently less explored. I suggest the authors look into the rich literature of geometry modeling in the computer graphics and vision community, and improve the paper by drawing inspiration from the latest progress there.
assistant
 This paper proposed a 3D shape generation model. The model is essentially an auto-encoder. The authors explored a new way of interpolation among encoded latent vectors, and drew connections to object functionality. The paper is, unfortunately, clearly below the bar of ICLR in many ways. It’s technically incremental: the paper doesn’t propose a new model; it instead suggests new way of interpolating the latent vectors for shape generation. The incremental technical innovation is not well-motivated or justified, either: the definitions of new concepts such as ‘functional essence’ and ‘importance vector’ are ad-hoc. The results are poor, much worse compared with the state-of-the-art shape synthesis methods. The writing and organization can also be improved. For example, the main idea should be emphasized first in the method section, and the detailed network architecture can be saved for a separate subsection or supplementary material. It’s good that the authors are looking into the direction of modeling shape functionality. This is an importance area that is currently less explored. I suggest the authors look into the rich literature of geometry modeling in the computer graphics and vision community, and improve the paper by drawing inspiration from the latest progress there.