Tensor based non-photo realistic rendering for Korean architectural images

Existing pen and ink sketch technologies can be applied to general images, but they could not produce optimal output for images of traditional architecture, because most images consist of exquisite straight lined patterns in traditional architecture, such as root tiles and window bars. The lines of roofs and eaves need to be described delicately to express pen and ink sketch most effectively. Therefore, by proposing a method to create white noise for light and shade of input images, to extract input vector directions from the white noise, and to determine the direction and length of stroke, a new expression technique is proposed for pen and ink sketch that could best reflect the characteristics of traditional architecture.

A hybrid rendering algorithm for textile objects

Although realistic textile rendering has been widely used in virtual garment and try-on systems, a robust method to simulate textile with a realistic appearance and high fidelity is yet to be established. We propose to use a novel hybrid geometric- and image-based rendering （GIBR） method to achieve photo realistic representation of textile products. The image-based technique, with its radiance synthesis algorithm, enables us to recover the reflectance properties of textile in an environment photo, and thus can render the appearance of textile material. The geometry-based technique, with its traditional illumination model of assigning illumination parameters extracted from the original scene （such as radiance and chroma dispatch）, makes it possible to interactively manipulate 3D virtual objects in the ＂real＂ environment. Our realistic textile rendering method has advantages over the traditional ones in its easiness to implement and its wide range of applications.

Statistical learning based facial animation

To synthesize real-time and realistic facial animation, we present an effective algorithm which combines image- and geometry-based methods for facial animation simulation. Considering the numerous motion units in the expression coding system, we present a novel simplified motion unit based on the basic facial expression, and construct the corresponding basic action for a head model. As image features are difficult to obtain using the performance driven method, we develop an automatic image feature recognition method based on statistical learning, and an expression image semi-automatic labeling method with rotation invariant face detection, which can improve the accuracy and efficiency of expression feature identification and training. After facial animation redirection, each basic action weight needs to be computed and mapped automatically. We apply the blend shape method to construct and train the corresponding expression database according to each basic action, and adopt the least squares method to compute the corresponding control parameters for facial animation. Moreover, there is a pre-integration of diffuse light distribution and specular light distribution based on the physical method, to improve the plausibility and efficiency of facial rendering. Our work provides a simplification of the facial motion unit, an optimization of the statistical training process and recognition process for facial animation, solves the expression parameters, and simulates the subsurface scattering effect in real time. Experimental results indicate that our method is effective and efficient, and suitable for computer animation and interactive applications.