Abstract: A computer system to implement automated parametric modeling of a built environment and simulating applying a plurality of sustainability metrics to respective modeled built environments.

Abstract: In various embodiments, a visualization engine generates graphs that facilitate sense making operations on data sets. A graph includes nodes that are associated with a data set and edges that represent relationships between the nodes. In operation, the visualization engine computes pairwise similarities between the nodes. Subsequently, the visualization engine computes a layout for the graph based on the pairwise similarities and user-specified constraints. Finally, the visualization engine renders a graph for display based on the layout, the nodes, and the edges. Advantageously, by interactively specifying constraints and then inspecting the topology of the automatically generated graph, the user may efficiently explore salient aspects of the data set.

Abstract: In various embodiments, a visualization engine generates graphs that facilitate sense making operations on data sets. A graph includes nodes that are associated with a data set and edges that represent relationships between the nodes. In operation, the visualization engine computes pairwise similarities between the nodes. Subsequently, the visualization engine computes a layout for the graph based on the pairwise similarities and user-specified constraints. Finally, the visualization engine renders a graph for display based on the layout, the nodes, and the edges. Advantageously, by interactively specifying constraints and then inspecting the topology of the automatically generated graph, the user may efficiently explore salient aspects of the data set.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating presentations of views of building entities according to an interaction graph. One of the methods includes processing building information model data to identify a plurality of building entities, wherein each building entity represents a respective feature of a building modeled by the data. Relationships between different respective pairs of the building entities are determined. The relationships are traversed according to simulated user interactions with respective presentations of a plurality of the building entities.

Abstract: Methods and apparatus for directional texture generation using image warping. An image warping directional texture generation method may, for example, be used to design hair or hairstyles. The method may obtain one or more user strokes via a provided brush or other user interface element to paint directional texture, such as hair, onto a target image. Primitives, such as strands, wisps, partings, etc., may be used to paint the directional texture. After the user applies a stroke to paint a primitive or a portion of a primitive onto the target image, the primitive may be automatically adjusted to fit the primitive to the stroke, handle distortion artifacts, and blend the primitive into the existing image. The user may continue to apply strokes until satisfied with the overall result.

Abstract: Methods and apparatus for procedural directional texture generation. A procedural directional texture generation method may, for example, be used to design hair or hairstyles. The method may obtain one or more strokes, one or more optional masks, and one or more optional user maps. One or more interpolated orientation maps may be generated from the input. The orientation maps, possibly along with one or more optional user maps, may be used to compute synthetic low-frequency lighting. A noise map may be generated at one or more frequencies and used, along with the interpolated maps, to generate high-frequency lighting. Alternatively, a flow-guided texture synthesis method may be used to generate high-frequency lighting. The low- and high-frequency lighting may then be combined to generate a desired frequency spectrum. Color may be added to the full structure, or alternatively color may be added at an earlier step.

Abstract: Methods and apparatus for directional texture generation using image warping. An image warping directional texture generation method may, for example, be used to design hair or hairstyles. The method may obtain one or more user strokes via a provided brush or other user interface element to paint directional texture, such as hair, onto a target image. Primitives, such as strands, wisps, partings, etc., may be used to paint the directional texture. After the user applies a stroke to paint a primitive or a portion of a primitive onto the target image, the primitive may be automatically adjusted to fit the primitive to the stroke, handle distortion artifacts, and blend the primitive into the existing image. The user may continue to apply strokes until satisfied with the overall result.

Abstract: Methods and apparatus for procedural directional texture generation. A procedural directional texture generation method may, for example, be used to design hair or hairstyles. The method may obtain one or more strokes, one or more optional masks, and one or more optional user maps. One or more interpolated orientation maps may be generated from the input. The orientation maps, possibly along with one or more optional user maps, may be used to compute synthetic low-frequency lighting. A noise map may be generated at one or more frequencies and used, along with the interpolated maps, to generate high-frequency lighting. Alternatively, a flow-guided texture synthesis method may be used to generate high-frequency lighting. The low- and high-frequency lighting may then be combined to generate a desired frequency spectrum. Color may be added to the full structure, or alternatively color may be added at an earlier step.

Abstract: Methods and apparatus for directional texture generation using flow-guided, sample-based texture synthesis. A texture synthesis directional texture generation method may, for example, be used to design hair or hairstyles. The method may obtain one or more strokes, one or more optional masks, one or more optional user maps, and directional texture samples. A target region and one or more samples may be divided into patches. For each patch in the target region, a matching patch from the samples may be located according to one or more features to generate a similarity map. The patches in the target region may then be replaced with the patches from the samples according to the similarity map. To match sample patches to target region patches based on features such as color and brightness, a procedural directional texture generation method may be used as a pre-processing step.