<p dir="ltr">3D effect synthesis is crucial in image editing to enhance realism or visual appeal. Unlike classical graphics rendering, which relies on complete 3D geometries, 3D effect synthesis in im- age editing operates solely with 2D images as inputs. This shift presents significant challenges, primarily addressed by data-driven methods that learn to synthesize 3D effects in an end-to-end manner. However, these methods face limitations in the diversity of 3D effects they can produce and lack user control. For instance, existing shadow generation networks are restricted to produc- ing hard shadows without offering any user input for customization.</p><p dir="ltr">In this dissertation, we tackle the research question: <i>how can we synthesize controllable and realistic 3D effects in image editing when only 2D information is available? </i>Our investigation leads to four contributions. First, we introduce a neural network designed to create realistic soft shadows from an image cutout and a user-specified environmental light map. This approach is the first attempt in utilizing neural network for realistic soft shadow rendering in real-time. Second, we develop a novel 2.5D representation Pixel Height, tailored for the nuances of image editing. This representation not only forms the foundation of a new soft shadow rendering pipeline that provides intuitive user control, but also generalizes the soft shadow receivers to be general shadow receivers. Third, we present the mathematical relationship between the Pixel Height representation and 3D space. This connection facilitates the reconstruction of normals or depth from 2D scenes, broadening the scope for synthesizing comprehensive 3D lighting effects such as reflections and refractions. A 3D-aware buffer channels are also proposed to improve the synthesized soft shadow quality. Lastly, we introduce Dr.Bokeh, a differentiable bokeh renderer that extends traditional bokeh effect algorithms with better occlusion modeling to correct flaws existed in existing methods. With the more precise lens modeling, we show that Dr.Bokeh not only achieves the state-of-the-art bokeh rendering quality, but also pushes the boundary of depth-from-defocus problem.</p><p dir="ltr">Our work in controllable 3D effect synthesis represents a pioneering effort in image editing, laying the groundwork for future lighting effect synthesis in various image editing applications. Moreover, the improvements to filtering-based bokeh rendering could significantly enhance com- mercial products, such as the portrait mode feature on smartphones.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25588062 |
| Date | 15 April 2024 |
| Creators | Yichen Sheng (18184378) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Controllable_3D_Effects_Synthesis_in_Image_Editing/25588062 |
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