High-quality Shape Synthesis with User-guided Deep Neural Networks

通过用户引导的深度神经网络进行高质量形状合成

• 批准号: RGPIN-2022-04903
• 负责人: vanKaick, Oliver
• 金额: $ 2.11万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750391
• 关键词: quality; Shape; Synthesis; User; guided

The long-term goal of my research program is to develop methods for facilitating the creation of computer graphics content, especially 3D shapes. 3D content is important in a variety of applications, such as computer games, movies and animation, and architectural simulations. In the next period of funding, I propose to develop computational methods for synthesizing 3D shapes with deep neural networks (DNNs), where the synthesis can be controlled by users and the generated shapes are of high-quality. These methods can then be applied to facilitate the generation of different types of 3D content. Content creation is challenging since high-quality 3D shapes are commonly created by explicitly modeling the geometry of the shapes. This process involves the use of complex user interfaces by skilled artists in a time-consuming process requiring substantial training. Thus, during the last two decades, computer graphics research has also proposed approaches for facilitating the modeling of 3D shapes by non-expert users, such as parametric models and sketching interfaces. However, many of these methods either still require sufficient artistic skills from the users, or require considerable manual work for pre-processing of the data. In recent years, methods for synthesizing 3D content based on machine learning have sparked much interest, especially methods based on deep neural networks (DNNs), since DNNs offer several advantages over traditional methods, such as easier training data preparation, no need to handcraft feature extraction methods, high generalization capabilities, and generative models that allow users to synthesize new data resembling the training data. However, DNNs currently have certain limitations that prevent them from being easily used for shape modeling. In this context, the goal of my research program for the next period of funding is to investigate solutions for improving the analysis and synthesis of 3D shapes with DNNs. In more detail: (1) We propose to develop methods for enabling more direct user control in the generation of 3D shapes with DNNs, so that users are able to design shapes according to their goals; (2) We propose to use representations that allow to generate higher-quality shapes compared to the state-of-the-art, such as representations based on procedural models that generate editable shapes with low complexity; (3) We propose to use procedural models to generate synthetic data, which can be used for training shape analysis DNNs with less manually-prepared data, or for evaluating DNN-based methods in controlled settings. The significance of the proposed work is that the developed solutions will enable the guided synthesis of high-quality shapes with less manual work. This will result in new software tools that will improve the current practices in industries that require shape modeling, allowing users of this technology to create a diversity and volume of content never seen before with reduced costs.

我的研究计划的长期目标是开发用于促进计算机图形内容（尤其是3D形状）的方法。 3D内容在各种应用程序中很重要，例如计算机游戏，电影和动画以及架构模拟。在下一个资金阶段，我建议开发与深神经网络（DNNS）合成3D形状的计算方法，其中可以由用户控制合成，并且生成的形状具有高质量。然后可以应用这些方法来促进不同类型的3D含量的生成。 内容创建具有挑战性，因为高质量的3D形状通常是通过明确建模形状的几何形状来创建的。此过程涉及在需要大量培训的耗时的过程中熟练的艺术家使用复杂的用户界面。因此，在过去的二十年中，计算机图形研究还提出了促进非专家用户（例如参数模型和草图接口）建模的方法。但是，这些方法中的许多方法仍需要用户提供足够的艺术技能，或者需要大量的手动工作才能进行数据处理。近年来，基于机器学习的3D内容的方法引发了很多兴趣，尤其是基于深神经网络（DNNS）的方法，因为DNNS比传统方法具有多种优势，例如更轻松的培训数据准备，因此无需手工制作功能提取方法，高度的通用功能，并可以使高度的通用模型以及可以使用新的数据进行培训数据，从而可以使用新的数据来培训。但是，DNN当前具有某些限制，可以防止它们轻松用于形状建模。在这种情况下，我的下一个资金研究计划的目的是研究改善与DNN的3D形状分析和合成的解决方案。更详细地说：（1）我们建议开发用于使用DNN生成3D形状的更直接用户控制的方法，以便用户能够根据其目标设计形状； （2）我们建议使用允许与最先进的表示形状产生更高形状的表示形式，例如基于基于程序模型的表示形式，这些模型产生了较低的复杂性的可编辑形状； （3）我们建议使用程序模型生成合成数据，可用于训练形状分析DNN，具有较少的手动编码数据，或评估受控设置中的基于DNN的方法。拟议的工作的意义在于，开发的解决方案将通过减少手动工作的指导综合高质量形状的综合。这将产生新的软件工具，这些工具将改善需要形状建模的行业中的当前实践，从而使该技术的用户能够创造出以降低成本从未见过的多样性和内容量。