Developing an Image-Based 3D Model Editing Method

Abstract

As 3D technologies advance rapidly, 3D printing, 3D animation, and 3D Movie are springing out in different areas. It becomes a remarkable challenge to generate a large number of 3D models effectively and efficiently. This paper proposes a novel editing method based on the feature lines of images (i.e., image contour and principal axis) for generating new 3D models. Our method takes as input an existing 3D model (as the original model) and an image selected by the user (or a sketch hand-drawn by the users), and performs the model editing to generate a new 3D model. In particular, our method first takes as input an original 3D model and an image selected by the user. Second, the selected image is processed to produce the feature lines, i.e., contour and principal axis of the image. Third, the silhouette of the original model from a given view is acquired, and projected on a projection plane to produce a contour and principal axis of the model, which is the feature lines of the original model. Fourth, by comparing the feature lines of the image and the original model, the constraint conditions are established to control the editing of the 3D model. Finally, 3D model editing is conducted through the as-rigid-as-possible mesh deformation to produce a new 3D model with the appearance resembling the selected image. Furthermore, this paper proposes an energy function to guide the detailed model editing, and measure the similarity between the generated 3D model and the corresponding image. We have conducted extensive experiments to evaluate the proposed method. The results show that comparing with the existing editing methods in literature, the proposed model editing method is able to construct various types of 3D models more effectively and more efficiently.

Color Face Image Generation with Improved Generative Adversarial Networks

“…The datasets used to train the generative model include the real portrait photos and the corresponding sketch images. The CelebA-HQ dataset, containing about 30,000 images of 1024 × 1024, is a collection of portraits of American celebrities [47][48][49][50]. In this study, the CelebA-HQ dataset was used as the image source of the self-built dataset to create sketch images.…”