We present an easy-to-use image retouching technique for realistic reshaping of human bodies in a single image. A model-based approach is taken by integrating a 3D whole-body morphable model into the reshaping process to achieve globally consistent editing effects. A novel body-aware image warping approach is introduced to reliably transfer the reshaping effects from the model to the image, even under moderate fitting errors. Thanks to the parametric nature of the model, our technique parameterizes the degree of reshaping by a small set of semantic attributes, such as weight and height. It allows easy creation of desired reshaping effects by changing the full-body attributes, while producing visually pleasing results even for loosely-dressed humans in casual photographs with a variety of poses and shapes.

High-quality normal maps are important intermediates for representing complex shapes. In this paper, we propose an interactive system for generating normal maps with the help of deep learning techniques. Utilizing the Generative Adversarial Network (GAN) framework, our method produces high quality normal maps with sketch inputs. In addition, we further enhance the interactivity of our system by incorporating user-specified normals at selected points. Our method generates high quality normal maps in real time. Through comprehensive experiments, we show the effectiveness and robustness of our method. A thorough user study indicates the normal maps generated by our method achieve a lower perceptual difference from the ground truth compared to the alternative methods.

No abstract

Decorative patterns are observed in many forms of art, typically enriching the visual aspect of otherwise simple shapes. Such patterns are especially difficult to create, as they often exhibit intricate structural details and at the same time have to precisely match the size and shape of the underlying geometry. In the field of Computer Graphics, several approaches have been proposed to automatically synthesize a decorative pattern along a curve, from an example. This empowers non expert users with a simple brush metaphor, allowing them to easily paint complex structured decorations. We extend this idea to the space of design and fabrication. The major challenge is to properly account for the topology of the produced patterns. In particular, our technique ensures that synthesized patterns will be made of exactly one connected component, so that once printed they form a single object. To achieve this goal we propose a two steps synthesis process, first synthesizing the topology of the pattern and later synthesizing its exact geometry. We introduce topology descriptors that efficiently capture the topology of the pattern synthesized so far. We propose several applications of our method, from designing objects using synthesized patterns along curves and within rectangles, to the decoration of surfaces with a dedicated smooth frame interpolation. Using our technique, designers paint structured patterns that can be fabricated into solid, tangible objects, creating unusual and surprising designs of lamps, chairs and laces from examples.