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dc.contributor.authorJahan, Tansinen_US
dc.contributor.authorGuan, Yanranen_US
dc.contributor.authorKaick, Oliver vanen_US
dc.contributor.editorMitra, Niloy and Viola, Ivanen_US
dc.date.accessioned2021-04-09T07:59:57Z
dc.date.available2021-04-09T07:59:57Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.142619
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf142619
dc.description.abstractWe 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectShape modeling
dc.titleSemantics-Guided Latent Space Exploration for Shape Generationen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersGenerative Models
dc.description.volume40
dc.description.number2
dc.identifier.doi10.1111/cgf.142619
dc.identifier.pages115-126


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