In this article we present Zahir, a framework for experimentation in Computer Graphics that provides a group of object-oriented base components that take care of common tasks in rendering techniques and algorithms, specially those of Non Photo-realistic Rendering (NPR). These components allow developers to implement rendering techniques and algorithms over static and animated meshes. Currently, Zahir is being used in a Master's Thesis and as support material in the undergraduate Computer Graphics course in University of Chile.
Another similar tool is @cite_3 . FreeStyle is a stylized line-renderer for 3D models. It's different from RTSC, because while RTSC extracts contours as iso-curves from surfaces, FreeStyle extracts silhouette and crease edges. The extracted edges are connected and stylized using procedures defined by the user. This software produces pretty pictures and is very customizable, but is not suited for interactive applications. On the other hand, it's very flexible and it can be used by artists because the styling can be written in the Python programming language.
@cite_3
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This article introduces a programmable approach to nonphotorealistic line drawings from 3D models, inspired by programmable shaders in traditional rendering. This approach relies on the assumption generally made in NPR that style attributes (color, thickness, etc.) are chosen depending on generic properties of the scene such as line characteristics or depth discontinuities, etc. We propose a new image creation model where all operations are controlled through user-defined procedures in which the relations between style attributes and scene properties are specified. A view map describing all relevant support lines in the drawing and their topological arrangement is first created from the 3D model so as to ensure the continuity of all scene properties along its edges; a number of style modules operate on this map, by procedurally selecting, chaining, or splitting lines, before creating strokes and assigning drawing attributes. Consistent access to properties of the scene is provided from the different elements of the map that are manipulated throughout the whole process. The resulting drawing system permits flexible control of all elements of drawing style: First, different style modules can be applied to different types of lines in a view; second, the topology and geometry of strokes are entirely controlled from the programmable modules; and third, stroke attributes are assigned procedurally and can be correlated at will with various scene or view properties. We illustrate the components of our system and show how style modules successfully encode stylized visual characteristics that can be applied across a wide range of models. This paper presents a new non-photorealistic stroke-based rendering (NPR SBR) framework for the stylization of videos featuring head shots of people, such as home videos, movies, and camera mobile phone clips. Spatiotemporal skin and edge detection are used to locate and emphasize the semantic content in the stylization process. The SBR portion of the algorithm features novel techniques for motion expression with elliptical brush strokes, brush stroke anchor point distribution, spatio-temporal color-sampling, and brush stroke animation with regard to state-of-the-art issues such as object occlusion and uncovering in the source video. A wide user-accessible parameter space and finishing touches such as cartoon-like edge decoration and other quirky effects empowers a variety of artistic outputs. The resulting stylized sequences are fun and interesting with regard to compression, summarization, motion visualization, story-boarding and art. Both the semantic content, and underlying video motion is highlighted and summarized on every frame of the stylized output sequence. Videos always exhibit various pattern motions, which can be modeled according to dynamics between adjacent frames. Previous methods based on linear dynamic system can model dynamic textures but have limited capacity of representing sophisticated nonlinear dynamics. Inspired by the nonlinear expression power of deep autoencoders, we propose a novel model named dynencoder which has an autoencoder at the bottom and a variant of it at the top (named as dynpredictor). It generates hidden states from raw pixel inputs via the autoencoder and then encodes the dynamic of state transition over time via the dynpredictor. Deep dynencoder can be constructed by proper stacking strategy and trained by layer-wise pre-training and joint fine-tuning. Experiments verify that our model can describe sophisticated video dynamics and synthesize endless video texture sequences with high visual quality. We also design classification and clustering methods based on our model and demonstrate the efficacy of them on traffic scene classification and motion segmentation. Inverse graphics attempts to take sensor data and infer 3D geometry, illumination, materials, and motions such that a graphics renderer could realistically reproduce the observed scene. Renderers, however, are designed to solve the forward process of image synthesis. To go in the other direction, we propose an approximate differentiable renderer (DR) that explicitly models the relationship between changes in model parameters and image observations. We describe a publicly available OpenDR framework that makes it easy to express a forward graphics model and then automatically obtain derivatives with respect to the model parameters and to optimize over them. Built on a new auto-differentiation package and OpenGL, OpenDR provides a local optimization method that can be incorporated into probabilistic programming frameworks. We demonstrate the power and simplicity of programming with OpenDR by using it to solve the problem of estimating human body shape from Kinect depth and RGB data.