Authoring consistent, animated ecosystems : Efficient learning from partial data
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2020-12-03
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Abstract
With recent increases in computing power, virtual worlds are now larger and more complex than ever. As such content becomes widespread in many different media, the expectation of realism has also dramatically increased for the end user. As a result, a large body of work has been accomplished on the modeling and generation of terrains and vegetation, sometimes also considering their interactions. However, animals have received far less attention and are often considered in isolation. Along with a lack of authoring tools, this makes the modeling of ecosystems difficult for artists, who are either limited in their creative freedom or forced to break biological realism.In this thesis, we present new methods suited to the authoring of ecosystems, allowing creative freedom without discarding biological realism. We provide data-centered tools for an efficient authoring, while keeping a low data requirement. By taking advantage of existing biology knowledge, we are able to guarantee both the consistency and quality of the results. We present dedicated methods for precise and intuitive instantiation of static and animated elements. Since static elements, such as vegetation, can exhibit complex interactions, we propose an accurate example-based method to synthesize complex and potentially overlapping arrangements. We apply a similar concept to the authoring of herds of animals, by using photographs or videos as input for example-based synthesis. At a larger scale, we use biological data to formulate a unified pipeline handling the global instantiation and long-term interactions of vegetation and animals. While this model enforces biological consistency, we also provide control by allowing manual editing of the data at any stage of the process.Our methods provide both user control and realism over the entire ecosystem creation pipeline, covering static and dynamic elements, as well as interactions between themselves and their environment. We also cover different scales, from individual placement and movement of elements to management of the entire ecosystem. We validate our results with user studies and comparisons with both real and expert data.
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