Gölge Yönetici Programı: turizm sektörü için geleceğin yöneticilerini yetiştiren kuluçka programı

The hospitality industry needs qualified work-ready graduates with skills but unfortunately tourism students are changing their career paths outside tourism and/or cannot obtain a managerial perspective. The Management Shadowing Program joins up the dots between academia, graduates and practitioners with real-life experiences from the management perspective, which opens a new window of opportunity for tourism graduates in a preference towards a career in tourism. This program enables theoretical in-class learning and summer internship practical experience at a anagerial level, leading to the utilization of data within practical contexts. The holistic multiple explanatory case study method utilized in this study reveals how industry-academia cooperation can effectively produce prospective managers of hospitality. Semi-structured interviews, focus group, weekly report content analysis, participant observations and questionnaires were used for the triangulation of data collection and analysis. The aim of this study is to propose a model for a Management Shadowing Program that can be used as an incubator for prospective graduates with the clear target of developing their managerial skills via experiential learning and creating a talent pool for the industry, as well as assurance of curriculum for academia and to evaluate the outcomes of the program from the perspectives of both alumni and the sector.Konaklama endüstrisi, yetenekli, işe hazır ve nitelikli mezunlara ihtiyaç duyarken maalesef, turizm öğrencileri kariyerlerini turizm dışındaki sektörlere yöneltiyorlar ve/veya eğitimleri sırasında yönetimsel bakış açısı elde edemiyor. Gölge Yönetici Programı, turizm mezunları için turizm alanında kariyer yapmayı tercih etmeleri için yeni bir fırsat penceresi açmaya yöneliktir ve mezunlar, akademisyenler ve uygulayıcılar arasındaki noktaları yönetim perspektifinden gerçek hayat deneyimleriyle birleştirmektedir. Program, yönetsel düzeyde sınıf içi öğrenme ve yaz stajı pratik tecrübelerini birleştirmeyi sağlayarak gerçek hayatta yöneticilik deneyimleri edinilmesine olanak sağlar. Bu çalışmada kullanılan bütünsel çoklu açıklayıcı vaka çalışması yöntemi, sektör-akademi işbirliği ile potansiyel yöneticilerini nasıl etkili bir şekilde üretebileceğini ortaya koymaktadır. Veri toplama ve analizlerinin çeşitlendirilmesinde yarı yapılandırılmış görüşmeler, odak grup çalışması, haftalık rapor içerik analizi, katılımcı gözlemleri ve anketler kullanılmaktadır. Bu çalışma, Gölge Yönetici Programının amacı sektör için yetenek havuzu yaratmak, akademi için müfredatın sektörün ihtiyaçlarına uyumunu sağlamaya yönelik bir model önerisi geliştirmeyi ve mezunların yönetsel beceriler geliştirmek için fırsat sunan bir yönetici kuluçka programnı hem müstakbel mezunlar hem sector gözünden programın başarısının değerlendirilmesidir.No sponso

3D mesh animation system targeted for multi-touch environments

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 74-78.Fast developments in computer technology have given rise to different application areas such as multimedia, computer games, and Virtual Reality. All these application areas are based on animation of 3D models of real world objects. For this purpose, many tools have been developed to enable computer modeling and animation. Yet, most of these tools require a certain amount of experience about geometric modeling and animation principles, which creates a handicap for inexperienced users. This thesis introduces a solution to this problem by presenting a mesh animation system targeted specially for novice users. The main approach is based on one of the fundamental model representation concepts, Laplacian framework, which is successfully used in model editing applications. The solution presented perceives a model as a combination of smaller salient parts and uses the Laplacian framework to allow these parts to be manipulated simultaneously to produce a sense of movement. The interaction techniques developed enable users to carry manipulation and global transformation actions at the same time to create more pleasing results. Furthermore, the approach utilizes the multi-touch screen technology and direct manipulation principles to increase the usability of the system. The methods described are experimented by creating simple animations with several 3D models; which demonstrates the advantages of the proposed solution.Ceylan, DuyguM.S

Motion Guided Deep Dynamic 3D Garments

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative rollout prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.Comment: 11 page

Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling

We present a method that enables synthesizing novel views and novel poses of arbitrary human performers from sparse multi-view images. A key ingredient of our method is a hybrid appearance blending module that combines the advantages of the implicit body NeRF representation and image-based rendering. Existing generalizable human NeRF methods that are conditioned on the body model have shown robustness against the geometric variation of arbitrary human performers. Yet they often exhibit blurry results when generalized onto unseen identities. Meanwhile, image-based rendering shows high-quality results when sufficient observations are available, whereas it suffers artifacts in sparse-view settings. We propose Neural Image-based Avatars (NIA) that exploits the best of those two methods: to maintain robustness under new articulations and self-occlusions while directly leveraging the available (sparse) source view colors to preserve appearance details of new subject identities. Our hybrid design outperforms recent methods on both in-domain identity generalization as well as challenging cross-dataset generalization settings. Also, in terms of the pose generalization, our method outperforms even the per-subject optimized animatable NeRF methods. The video results are available at https://youngjoongunc.github.io/ni