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This paper gives an overview of computer graphics representations of trees commonly used for the rendering of complex scene of vegetation. Looking for the right compromise between realism and efficiency has lead researchers to consider various types of geometrical plant models with different types of complexity. To achieve realist plant model, a complex structure of plant with full details is generally considered. In contrast, to promote efficiency, other approaches summarize plant geometry with few primitives allowing rapid rendering. Finally, to find a good compromise, structures with adaptive complexity are defined. Theses different types of representations and the ways to use them are presented, classified and discussed. The proposed classification principles rely on the type of structural details used in the plants representations. Characterization of all these methods is completed with various additional criteria including rendering primitive type, distance validity, interactive possibilities, animation ability and lighting properties.

Flowers are familiar in virtual scenes, however, the design of flower patterns is still mainly done by hand. To produce a number of flower color patterns required by a large scene can be very labor-consuming. In this paper, we present a biologically-motivated algorithm for modeling and visualization of flower color patterns. It is able to produce various flower color patterns with little user interaction. In our system, pigmentation is simulated by a modified reaction-diffusion system, and the simulation function is controlled by a few experiential parameters. Thus it can be adjusted to produce various flower color patterns widely observed in the real world. Furthermore, our algorithm can be easily embedded into other advanced shading models to improve the quality of their rendering results. We present an image-based texture generation method as an example. 1

We present a novel technique for interactive, intuitive, and efficient modeling of virtual plants and plant ecosystems. Our approach is biologically-based, but shades the user from overwhelming input parameters by simplifying them to intuitive controls. Users are able to create scenes that are populated by virtual plants. Plants communicate actively with the environment and attempt to generate an optimal spatial distribution that dynamically adapts to neighboring plants, to user defined obstacles, light, and gravity. We demonstrate simulations of ecosystems composed of up to 140 trees that are computed in less than two minutes. Various phenomena previously available for non-realtime procedural approaches are created interactively, such as plants competing for space, topiary, plant lighting, virtual forests, etc. Results are aimed at architectural modeling, the entertainment industry, and everywhere that quick and fast creation of believable biological plant models is necessary.

Highly detailed natural scenes and objects tend to be perceived as being realistic, while repeated parts and patterns decrease their realism. To avoid scenes with noticeable repeated elements, we introduce the notion of ’more of the same’, which focuses on the task of generating additional similar instances from a small set of exemplars. The small number of exemplars, as well as their diversity and detailed structural texture, makes it difficult to apply statistical methods, or other machine learning tools, and thus more specialized tools need to be used. In this paper, we focus on generating a rich variation of highly detailed realistic leaves from just a handful set of examples. The method that we present does use only minimal domain specific knowledge and requires only minimal user assistance applied on a single training leaf exemplar to extract and separate structural layers. The knowledge from one leaf is then transferred to the other exemplars by a novel color/spatial layer inducing algorithm. The premise of structural layering is that each set of layers is simple enough to be synthesized separately and then composed into a novel leaf structural texture. This composition also allows the synthesis of slightly modified layers from the set of examples, which can generate a large set of differently looking leaves. We demonstrate numerous results of realistically looking leaves produced by our method from a small set of leaves.