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Procedural modeling is used across many industries for rapid 3D content creation. However, professional procedural tools often lack artistic control, requiring manual edits on baked results, diminishing the advantages of a procedural modeling pipeline. Previous approaches to enable local artistic control require special annotations of the procedural system and manual exploration of potential edit locations. Therefore, we propose a novel approach to discover meaningful and non‐redundant good edit locations (GELs). We introduce a bottom‐up algorithm for finding GELs directly from the attributes in procedural models, without special annotations. To make attribute edits at GELs persistent, we analyze their local spatial context and construct a meta‐locator to uniquely specify their structure. Meta‐locators are calculated independently per attribute, making them robust against changes in the procedural system. Functions on meta‐locators enable intuitive and robust multi‐selections. Finally, we introduce an algorithm to transfer meta‐locators to a different procedural model. We show that our approach greatly simplifies the exploration of the local edit space, and we demonstrate its usefulness in a user study and multiple real‐world examples.

Figure 1: In procedural modeling, a single rule set can produce a wide variety of 3D models (left). This paper presents a thumbnail gallery generation system which automatically samples a rule set, clusters the resulting models into distinct groups (middle), and selects a representative image for each group to visualize the diversity of the rule set (right). AbstractProcedural modeling allows for the generation of innumerable variations of models from a parameterized, conditional or stochastic rule set. Due to the abstractness, complexity and stochastic nature of rule sets, it is often very difficult to have an understanding of the diversity of models that a given rule set defines. We address this problem by presenting a novel system to automatically generate, cluster, rank, and select a series of representative thumbnail images out of a rule set. We introduce a set of 'view attributes' that can be used to measure the suitability of an image to represent a model, and allow for comparison of different models derived from the same rule set. To find the best thumbnails, we exploit these view attributes on images of models obtained by stochastically sampling the parameter space of the rule set. The resulting thumbnail gallery gives a representative visual impression of the procedural modeling potential of the rule set. Performance is discussed by means of a number of distinct examples and compared to state-of-the-art approaches.

We introduce a light-weight multimedia composition layer which is situated on top of existing music, sound, and graphics frameworks and libraries. The layer provides applications with a programming interface for multimedia composition, automating real-time media processing and synchronisation. Our work facilitates the mapping of media frameworks to a unified processing graph. We present a graph segmentation algorithm that solves the problem of communication between threads of multiple frameworks while providing global consistency and without affecting processing performance. In addition, support for logical and physical time is included and enables framework-independent realisation of complex multimedia designs. The paper discusses the layer's architecture in detail and shows how multimedia applications can efficiently exploit its capabilities.

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