31-Issue 1
https://diglib.eg.org:443/handle/10.2312/170
Regular Issue2024-03-28T15:42:29ZShiny Vase, Translucent Candle and Metallic Sculpture
https://diglib.eg.org:443/handle/10.1111/v31i1pp217-218
Shiny Vase, Translucent Candle and Metallic Sculpture
Bousseau, Adrien; Chapoulie, Emmanuelle; Ramamoorthi, Ravi; Agrawala, Maneesh
Holly Rushmeier and Oliver Deussen
2012-01-01T00:00:00ZEvent report: 2011 Eurographics Symposium on Parallel Graphics and Visualization
https://diglib.eg.org:443/handle/10.1111/v31i1pp216-216
Event report: 2011 Eurographics Symposium on Parallel Graphics and Visualization
Kuhlen, Torsten
Holly Rushmeier and Oliver Deussen
2012-01-01T00:00:00ZSpatio-Temporal Filtering of Indirect Lighting for Interactive Global Illumination
https://diglib.eg.org:443/handle/10.1111/v31i1pp189-201
Spatio-Temporal Filtering of Indirect Lighting for Interactive Global Illumination
Chen, Ying-Chieh; Lei, Su Ian Eugene; Chang, Chun-Fa
Holly Rushmeier and Oliver Deussen
We introduce a screen‐space statistical filtering method for real‐time rendering with global illumination. It is inspired by statistical filtering proposed by Meyer et al. to reduce the noise in global illumination over a period of time by estimating the principal components from all rendered frames. Our work extends their method to achieve nearly real-time performance on modern GPUs. More specifically, our method employs the candid covariance‐free incremental PCA to overcome several limitations of the original algorithm by Meyer et al., such as its high computational cost and memory usage that hinders its implementation on GPUs. By combining the reprojection and per‐pixel weighting techniques, our method handles the view changes and object movement in dynamic scenes as well.
2012-01-01T00:00:00ZGeneralized Model-Based Human Motion Recognition with Body Partition Index Maps
https://diglib.eg.org:443/handle/10.1111/v31i1pp202-215
Generalized Model-Based Human Motion Recognition with Body Partition Index Maps
Deng, Liqun; Leung, Howard; Gu, Naijie; Yang, Yang
Holly Rushmeier and Oliver Deussen
Content-based human motion analysis has captured extensive concerns of researchers from the domains of computer animation, human-machine interaction, entertainment, etc. However, it is a non-trivial task due to the spatial and temporal variations in the motion data. In this paper, we propose a generalized model (GM)-based approach to model the variations and accurately recognize motion patterns. We partition the human character model into five parts, and extract the features of the submotions of each specific body part using clustering techniques. These features from the training trials in each class are combined to build the GM. We propose a new penalty based similarity measure for DTW to be used with the GMs for isolated motion recognition. On the other hand, from the GMs five body partition index maps are constructed and used for matching together with a flexible end point detection scheme during continuous motion recognition. In the experiments, we examine the effectiveness and efficiency of the approach in both isolated motion and continuous motion recognition. The results show that our proposed method has good performance compared with other state-of-the-art methods in recognition accuracy and processing speed.
2012-01-01T00:00:00Z