Abstract
We present a deep neural framework that allows users to create surfaces from a stream of sparse 3D sketch strokes. Our network consists of a global surface estimation module followed by a local surface refinement. This facilitates in the incremental prediction of surfaces. Thus, our proposed method works with 3D sketch strokes and estimate a surface interactively in real time. We compare the proposed method with various state-of-the-art methods and show its efficacy for surface fitting. Further, we integrate our method into an existing Blender based 3D content creation pipeline to show its usefulness in 3D modelling.
Keywords
- Theory and philosophy of arts and creativity in AI systems: Computational paradigms, architectures and models for creativity
- Application domains: Other domains of art or creativity
- Theory and philosophy of arts and creativity in AI systems: Support of human creativity
