Computer Graphics

We used a DenseNet Convolutional Neural Network (CNN) architecture to perform the recognition in real-time, from both indoor and outdoor environments, not requiring any image segmentation process. Our research also generated a vocabulary, considering users’ preferences, seeking a set of natural and comfortable hand poses and evaluated users’ satisfaction and performance for an entertainment setup. Our recognition model achieved an accuracy of 97.89%.

we developed a novel Recurrent Neural Network topology based on Long Short-Term Memory cells (LSTMs) that are able to classify motions sequences of different sizes.

We present a novel approach that allows the user to produce both new views and new poses easily for the 2.5D Models, thus puppeteering the 2.5D Models. It makes use of a hierarchical bone structure that explores the methodology of the 2.5D Models, ensuring coherence between the bone structure and the model. The usability of the present approach is intuitive for users acquainted with previous 2.5D Modeling tools.

2.5D Cartoon Models shading and animation.

Deep Learning methods of lighting estimation.

In this work, we tackle the problem of providing interactive 3D shading effects to 2.5D modeling. Our technique relies on the graphics pipeline to infer relief and to simulate the 3D rotation of the shading effects inside the 2D models in real-time. We demonstrate the application on Phong, Gooch and cel shadings, as well as environment mapping, fur simulation, animated texture mapping, and (object-space and screen-space) texture hatchings.