Browsing by Author "Shen, I-Chao"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    ClipFlip : Multi‐view Clipart Design
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Shen, I‐Chao; Liu, Kuan‐Hung; Su, Li‐Wen; Wu, Yu‐Ting; Chen, Bing‐Yu; Benes, Bedrich and Hauser, Helwig
    We present an assistive system for clipart design by providing visual scaffolds from the unseen viewpoints. Inspired by the artists' creation process, our system constructs the visual scaffold by first synthesizing the reference 3D shape of the input clipart and rendering it from the desired viewpoint. The critical challenge of constructing this visual scaffold is to generate a reference 3D shape that matches the user's expectations in terms of object sizing and positioning while preserving the geometric style of the input clipart. To address this challenge, we propose a user‐assisted curve extrusion method to obtain the reference 3D shape. We render the synthesized reference 3D shape with a consistent style into the visual scaffold. By following the generated visual scaffold, the users can efficiently design clipart with their desired viewpoints. The user study conducted by an intuitive user interface and our generated visual scaffold suggests that our system is especially useful for estimating the ratio and scale between object parts and can save on average 57% of drawing time.
  • Thumbnail Image
    Item
    Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content‐based Guidance
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Chong, Toby; Shen, I‐Chao; Sato, Issei; Igarashi, Takeo; Benes, Bedrich and Hauser, Helwig
    Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain desired results. Existing attempts add interactivity but require either tailored architectures or extra data. We present a human‐in‐the‐optimization method that allows users to directly explore and search the latent vector space of generative image modelling. Our system provides multiple candidates by sampling the latent vector space, and the user selects the best blending weights within the subspace using multiple sliders. In addition, the user can express their intention through image editing tools. The system samples latent vectors based on inputs and presents new candidates to the user iteratively. An advantage of our formulation is that one can apply our method to arbitrary pre‐trained model without developing specialized architecture or data. We demonstrate our method with various generative image modelling applications, and show superior performance in a comparative user study with prior art iGAN [ZKSE16].