Abstract: A processor obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value and a second pixel value of the display. Upon detecting a region of the input image having an original pixel value above the threshold, the processor can create a data structure including a location of the region in the input image and an original pixel value of the region. The data structure occupies less memory than the input image. The display presents the input image including the region of the image having the original pixel value above the threshold. The processor sends the data structure to a camera, which records the presented image. The processor performing postprocessing obtains the data structure and the recorded image and increases dynamic range of the recorded image by modifying the recorded image based on the data structure.

Abstract: A processor obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value and a second pixel value of the display. Upon detecting a region of the input image having an original pixel value above the threshold, the processor can create a data structure including a location of the region in the input image and an original pixel value of the region. The data structure occupies less memory than the input image. The display presents the input image including the region of the image having the original pixel value above the threshold. The processor sends the data structure to a camera, which records the presented image. The processor performing postprocessing obtains the data structure and the recorded image and increases dynamic range of the recorded image by modifying the recorded image based on the data structure.

Abstract: A processor obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value and a second pixel value of the display. Upon detecting a region of the input image having an original pixel value above the threshold, the processor can create a data structure including a location of the region in the input image and an original pixel value of the region. The data structure occupies less memory than the input image. The display presents the input image including the region of the image having the original pixel value above the threshold. The processor sends the data structure to a camera, which records the presented image. The processor performing postprocessing obtains the data structure and the recorded image and increases dynamic range of the recorded image by modifying the recorded image based on the data structure.

Abstract: A processor obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value and a second pixel value of the display. Upon detecting a region of the input image having an original pixel value above the threshold, the processor can create a data structure including a location of the region in the input image and an original pixel value of the region. The data structure occupies less memory than the input image. The display presents the input image including the region of the image having the original pixel value above the threshold. The processor sends the data structure to a camera, which records the presented image. The processor performing postprocessing obtains the data structure and the recorded image and increases dynamic range of the recorded image by modifying the recorded image based on the data structure.

Abstract: A processor obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value and a second pixel value of the display. Upon detecting a region of the input image having an original pixel value above the threshold, the processor can create a data structure including a location of the region in the input image and an original pixel value of the region. The data structure occupies less memory than the input image. The display presents the input image including the region of the image having the original pixel value above the threshold. The processor sends the data structure to a camera, which records the presented image. The processor performing postprocessing obtains the data structure and the recorded image and increases dynamic range of the recorded image by modifying the recorded image based on the data structure.

Abstract: Techniques are disclosed for accounting for features of computer-generated dynamic or simulation models being at different scales. Some examples of dynamic or simulation models may include models representing hair, fur, strings, vines, tails, or the like. In various embodiments, features at different scales in a complex dynamic or simulation model can be treated differently when rendered and/or simulated.

Abstract: Techniques are disclosed for stably simulating stylized curly hair that address artistic needs and performance demands, both found in the production of feature films. To satisfy the artistic requirement of maintaining a curl's helical shape during motion, a hair model is developed based upon an extensible elastic rod. A method is provided for stably computing a frame along a hair curve for stable simulation of curly hair. The hair model introduces a new type of spring for controlling the bending and twisting of a curl and another for maintaining the helical shape during extension. The disclosed techniques address performance concerns often associated with handling hair-hair contact interactions by efficiently parallelizing the simulation. A novel algorithm is presented for pruning both hair-hair contact pairs and hair particles.

Abstract: Techniques are disclosed for accounting for features of computer-generated dynamic or simulation models being at different scales. Some examples of dynamic or simulation models may include models representing hair, fur, strings, vines, tails, or the like. In various embodiments, features at different scales in a complex dynamic or simulation model can be treated differently when rendered and/or simulated.

Abstract: Techniques are disclosed for accounting for features of computer-generated dynamic or simulation models being at different scales. Some examples of dynamic or simulation models may include models representing hair, fur, strings, vines, tails, or the like. In various embodiments, features at different scales in a complex dynamic or simulation model can be treated differently when rendered and/or simulated.

Abstract: Techniques are disclosed for orienting (or reorienting) properties of computer-generated models, such as those associated with dynamic models or simulation models. Properties (e.g., material or physical properties) that influence the behavior of a dynamic or simulation model (e.g., a complex curve model representing a curly hair) may be oriented or re-oriented as desired using readily available reference frames. These references frame may be obtained using a proxy model that corresponds to the dynamic or simulation model in a less computationally expensive manner in some embodiments than some techniques for determining reference frames directly using the dynamic or simulation model. In some embodiments, the proxy model may include a smoothed version of the dynamic or simulation model. In other embodiments, the proxy model may include a filtered or simplified version of the dynamic or simulation model.

Abstract: Techniques are disclosed for orienting (or reorienting) properties of computer-generated models, such as those associated with dynamic models or simulation models. Properties (e.g., material or physical properties) that influence the behavior of a dynamic or simulation model (e.g., a complex curve model representing a curly hair) may be oriented or re-oriented as desired using readily available reference frames. These references frame may be obtained using a proxy model that corresponds to the dynamic or simulation model in a less computationally expensive manner in some embodiments than some techniques for determining reference frames directly using the dynamic or simulation model. In some embodiments, the proxy model may include a smoothed version of the dynamic or simulation model. In other embodiments, the proxy model may include a filtered or simplified version of the dynamic or simulation model.

Abstract: Techniques are disclosed for accounting for features of computer-generated dynamic or simulation models being at different scales. Some examples of dynamic or simulation models may include models representing hair, fur, strings, vines, tails, or the like. In various embodiments, features at different scales in a complex dynamic or simulation model can be treated differently when rendered and/or simulated.

Abstract: A method for exporting animation data from a native animation environment to a non-native animation environment includes determining first object poses in response to a first object model in the native environment and animation variables, determining a second object model including a geometric object model, determining second object poses in response to the second object model and animation variables, determining surface errors between the first object poses and the second object poses, determining a corrective object offsets in response to the surface errors, determining actuation values associated with the corrective object offsets in response to the surface errors, determining a third object model compatible with the non-native animation environment in response to the second object of poses, the corrective offsets, and the actuation values, and storing the third object model in a memory.

Abstract: A method for a computer system includes determining a model for a first personality of a component of an object, wherein the model for the first personality of the component is associated with a component name and a first personality indicia, determining a model for a second personality of the component of the object, wherein the model for the second personality of the component is associated with the component name and the second personality indicia, determining a multiple personality model of the object, wherein the model of the object includes the model for the first personality of the component, the model of the second personality of the component, the first personality indicia, and the second personality indicia, and storing the multiple personality model of the object in a single file.

Abstract: A method for exporting animation data from a native animation environment to a non-native animation environment includes determining first object poses in response to a first object model in the native environment and animation variables, determining a second object model including a geometric object model, determining second object poses in response to the second object model and animation variables, determining surface errors between the first object poses and the second object poses, determining a corrective object offsets in response to the surface errors, determining actuation values associated with the corrective object offsets in response to the surface errors, determining a third object model compatible with the non-native animation environment in response to the second object of poses, the corrective offsets, and the actuation values, and storing the third object model in a memory.

Abstract: A volumetric representation of a hair simulation model determines collective hair attributes. To determine inter-hair collisions, vertices include average velocities of the adjacent portions of the model. The average velocities determine target velocities. Forces for the model are determined from the target velocity values. To direct hair to a desired pose, vertices include target and current density values representing the density of adjacent portions of the model in the desired pose and current position, respectively. The differences in density values determine pressure forces applied to the model. To determine the illumination of the hair, vertices include density values representing the density of adjacent portions of the model. The density values define a hair surface, and signed distance values relative to the surface are determined for the vertices. Normal vectors are determined from the gradients of the signed distance values at locations corresponding the positions of the hairs.

Abstract: A volumetric representation of a hair simulation model determines collective hair attributes. To determine inter-hair collisions, vertices include average velocities of the adjacent portions of the model. The average velocities determine target velocities. Forces for the model are determined from the target velocity values. To direct hair to a desired pose, vertices include target and current density values representing the density of adjacent portions of the model in the desired pose and current position, respectively. The differences in density values determine pressure forces applied to the model. To determine the illumination of the hair, vertices include density values representing the density of adjacent portions of the model. The density values define a hair surface, and signed distance values relative to the surface are determined for the vertices. Normal vectors are determined from the gradients of the signed distance values at locations corresponding the positions of the hairs.

Abstract: A method for rendering a plurality of geometrically thin objects in a computer system includes receiving a plurality of points associated with each of the plurality of geometrically thin objects, determining a plurality of volumetric point densities associated with the plurality of geometrically thin objects in response to the plurality of points, determining a plurality of volumetric point density gradients in response to the plurality of volumetric point densities, determining a plurality of surface normals associated with at least a subset of points in response to the plurality of volumetric point density gradients, and performing a rendering operation for the subset of points in response to the plurality of surface normals.

Abstract: A method for rendering hair particles includes determining a grid with vertices and voxels bounding the hair particles, determining hair densities for the vertices, smoothing the hair densities, solving a distance function to form a distance field in response to the smooth hair densities, wherein a distance function returns zero at a pre-determined hair particle density, determining a surface normal direction for a hair particle in response to the distance field values, determining a hair illumination value in response to a first illumination source, and determining a shading value for the hair particle using the hair illumination value and the surface normal direction for the hair particle.

Abstract: A method for a computer system includes determining a model for a first personality of a component of an object, wherein the model for the first personality of the component is associated with a component name and a first personality indicia, determining a model for a second personality of the component of the object, wherein the model for the second personality of the component is associated with the component name and the second personality indicia, determining a multiple personality model of the object, wherein the model of the object includes the model for the first personality of the component, the model of the second personality of the component, the first personality indicia, and the second personality indicia, and storing the multiple personality model of the object in a single file.