GAMES Webinar 2019 – 96期（SIGGRAPH 2019 报告） | 刘浩（中国科学技术大学），吴润迪（北京大学）
【GAMES Webinar 2019-96期】
报告题目：Computational Peeling Art Design
Some artists peel citrus fruits into a variety of elegant 2D shapes, depicting animals, plants, and cartoons. It is a creative art form, called Citrus Peeling Art. This art form follows the conservation principle, i.e., each shape must be created using one entire peel. Central to this art is finding optimal cut lines so that the citruses can be cut and unfolded into the desired shapes. However, it is extremely difficult for users to imagine and generate cuts for their desired shapes. To this end, we present a computational method for citrus peeling art designs. Our key insight is that instead of solving a difficult cut generation problem, we map a designed input shape onto a citrus in an attempt to cover the entire citrus and use the mapped boundary to generate the cut paths. Sometimes, a mapped shape is unable to completely cover a citrus. Consequently, we have developed five customized ways of interaction that are used to rectify the input shape so that it is suitable for citrus peeling art. The mapping process and user interactions are iteratively conducted to satisfy a user’s design intentions. A large number of experiments, including a formative user study, demonstrate the capability and practicability of our method for peeling art design and construction.
报告题目：Learning Character-Agnostic Motion for Motion Retargeting in 2D
Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer animation, is the retargeting of motion from one performer to another.
While humans move in three dimensions, the vast majority of human motions are captured using video, requiring 2D-to-3D pose and camera recovery, before existing retargeting approaches may be applied. In this paper, we present a new method for retargeting video-captured motion between different human performers, without the need to explicitly reconstruct 3D poses and/or camera parameters.
Rundi Wu is an undergraduate student of EECS, Peking University, with honor track Turing Class, supervised by Prof. Baoquan Chen. His research interests are Computer Graphics, Computer Vision and Deep Learning. Besides research work, he’s also one of the maintainers and active developer of TensorLayer 2.0.
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