Browsing by Author "Kaick, Oliver van"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Data-guided Authoring of Procedural Models of Shapes
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Hossain, Ishtiaque; Shen, I-Chao; Igarashi, Takeo; Kaick, Oliver van; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Procedural models enable the generation of a large amount of diverse shapes by varying the parameters of the model. However, writing a procedural model for replicating a collection of reference shapes is difficult, requiring much inspection of the original and replicated shapes during the development of the model. In this paper, we introduce a data-guided method for aiding a programmer in creating a procedural model to replicate a collection of reference shapes. The user starts by writing an initial procedural model, and the system automatically predicts the model parameters for reference shapes, also grouping shapes by how well they are approximated by the current procedural model. The user can then update the procedural model based on the given feedback and iterate the process. Our system thus automates the tedious process of discovering the parameters that replicate reference shapes, allowing the programmer to focus on designing the high-level rules that generate the shapes. We demonstrate through qualitative examples and a user study that our method is able to speed up the development time for creating procedural models of 2D and 3D man-made shapes.
  • Thumbnail Image
    Item
    Semantics-Guided Latent Space Exploration for Shape Generation
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Jahan, Tansin; Guan, Yanran; Kaick, Oliver van; Mitra, Niloy and Viola, Ivan
    We introduce an approach to incorporate user guidance into shape generation approaches based on deep networks. Generative networks such as autoencoders and generative adversarial networks are trained to encode shapes into latent vectors, effectively learning a latent shape space that can be sampled for generating new shapes. Our main idea is to enable users to explore the shape space with the use of high-level semantic keywords. Specifically, the user inputs a set of keywords that describe the general attributes of the shape to be generated, e.g., ''four legs'' for a chair. Then, our method maps the keywords to a subspace of the latent space, where the subspace captures the shapes possessing the specified attributes. The user then explores only this subspace to search for shapes that satisfy the design goal, in a process similar to using a parametric shape model. Our exploratory approach allows users to model shapes at a high level without the need for advanced artistic skills, in contrast to existing methods that allow to guide the generation with sketching or partial modeling of a shape. Our technical contribution to enable this exploration-based approach is the introduction of a label regression neural network coupled with shape encoder/decoder networks. The label regression network takes the user-provided keywords and maps them to distributions in the latent space. We show that our method allows users to explore the shape space and generate a variety of shapes with selected high-level attributes.