Abstract: The disclosed computer-implemented method includes systems for optimizing color rendition in an LED volume virtual production stage. For example, the described systems optimize or correct color rendition by applying a series of color correction matrices to color pixel values within the virtual production stage and to final captured imagery filmed within the virtual production stage. The described systems generate the color correction matrices from four calibration images taken within the virtual production stage. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Example embodiments relate to techniques for volumetric performance capture with neural rendering. A technique may involve initially obtaining images that depict a subject from multiple viewpoints and under various lighting conditions using a light stage and depth data corresponding to the subject using infrared cameras. A neural network may extract features of the subject from the images based on the depth data and map the features into a texture space (e.g., the UV texture space). A neural renderer can be used to generate an output image depicting the subject from a target view such that illumination of the subject in the output image aligns with the target view. The neural render may resample the features of the subject from the texture space to an image space to generate the output image.

Abstract: Apparatus and methods related to applying lighting models to images of objects are provided. An example method includes applying a geometry model to an input image to determine a surface orientation map indicative of a distribution of lighting on an object based on a surface geometry. The method further includes applying an environmental light estimation model to the input image to determine a direction of synthetic lighting to be applied to the input image. The method also includes applying, based on the surface orientation map and the direction of synthetic lighting, a light energy model to determine a quotient image indicative of an amount of light energy to be applied to each pixel of the input image. The method additionally includes enhancing, based on the quotient image, a portion of the input image. One or more neural networks can be trained to perform one or more of the aforementioned aspects.

Abstract: The disclosed computer-implemented method includes systems for optimizing color rendition in an LED volume virtual production stage. For example, the described systems optimize or correct color rendition by applying a series of color correction matrices to color pixel values within the virtual production stage and to final captured imagery filmed within the virtual production stage. The described systems generate the color correction matrices from four calibration images taken within the virtual production stage. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems, methods, and computer program products are described that include obtaining, at a processor, a first image from an image capture device onboard a computing device, detecting, using the processor and at least one sensor, a device orientation of the computing device associated with capture of the first image, determining, based on the device orientation and a tracking stack associated with the computing device, a rotation angle in which to rotate the first image, rotating the first image to the rotation angle to generate a second image, and generating neural network based estimates associated with the first image and the second image.

Abstract: Systems, methods, and computer program products are described that implement obtaining, at an electronic computing device and for at least one image of a scene rendered in an Augmented Reality (AR) environment, a scene lighting estimation captured at a first time period. The scene lighting estimation may include at least a first image measurement associated with the scene. The implementations may include determining, at the electronic computing device, a second image measurement associated with the scene at a second time period, determining a function of the first image measurement and the second image measurement. Based on the determined function, the implementations may also include triggering calculation of a partial lighting estimation update or triggering calculation of a full lighting estimation update and rendering, on a screen of the electronic computing device and for the scene, the scene using the partial lighting estimation update or the full lighting estimation update.

Abstract: Techniques of estimating lighting from portraits includes generating a lighting estimate from a single image of a face based on a machine learning (ML) system using multiple bidirectional reflection distribution functions (BRDFs) as a loss function. In some implementations, the ML system is trained using images of faces formed with HDR illumination computed from LDR imagery. The technical solution includes training a lighting estimation model in a supervised manner using a dataset of portraits and their corresponding ground truth illumination.

Abstract: An example method, apparatus, and computer-readable storage medium are provided to predict high-dynamic range (HDR) lighting from low-dynamic range (LDR) background images. In an example implementation, a method may include receiving low-dynamic range (LDR) background images of scenes, each LDR background image captured with appearance of one or more reference objects with different reflectance properties; and training a lighting estimation model based at least on the received LDR background images to predict high-dynamic range (HDR) lighting based at least on the trained model. In another example implementation, a method may include capturing a low-dynamic range (LDR) background image of a scene from an LDR video captured by a camera of the electronic computing device; predicting high-dynamic range (HDR) lighting for the image, the predicting, using a trained model, based at least on the LDR background image; and rendering a virtual object based at least on the predicted HDR lighting.

Abstract: Systems, methods, and computer program products are described that include obtaining, at a processor, a first image from an image capture device onboard a computing device, detecting, using the processor and at least one sensor, a device orientation of the computing device associated with capture of the first image, determining, based on the device orientation and a tracking stack associated with the computing device, a rotation angle in which to rotate the first image, rotating the first image to the rotation angle to generate a second image, and generating neural network based estimates associated with the first image and the second image.

Abstract: Systems, methods, and computer program products are described that include obtaining, at a processor, a first image from an image capture device onboard a computing device, detecting, using the processor and at least one sensor, a device orientation of the computing device associated with capture of the first image, determining, based on the device orientation and a tracking stack associated with the computing device, a rotation angle in which to rotate the first image, rotating the first image to the rotation angle to generate a second image, and generating neural network based estimates associated with the first image and the second image.

Abstract: Systems, methods, and computer program products are described that implement obtaining, at an electronic computing device and for at least one image of a scene rendered in an Augmented Reality (AR) environment, a scene lighting estimation captured at a first time period. The scene lighting estimation may include at least a first image measurement associated with the scene. The implementations may include determining, at the electronic computing device, a second image measurement associated with the scene at a second time period, determining a function of the first image measurement and the second image measurement. Based on the determined function, the implementations may also include triggering calculation of a partial lighting estimation update or triggering calculation of a full lighting estimation update and rendering, on a screen of the electronic computing device and for the scene, the scene using the partial lighting estimation update or the full lighting estimation update.

Abstract: Systems, methods, and computer program products are described that include obtaining, at a processor, a first image from an image capture device onboard a computing device, detecting, using the processor and at least one sensor, a device orientation of the computing device associated with capture of the first image, determining, based on the device orientation and a tracking stack associated with the computing device, a rotation angle in which to rotate the first image, rotating the first image to the rotation angle to generate a second image, and generating neural network based estimates associated with the first image and the second image.

Abstract: Systems and methods for lighting inserted content are provided. For example, the inserted content may include augmented reality content that is inserted into an image of a physical space. An example system and method may include determining a location within an image to insert content. For example, the image may be captured by a camera device. The example system and method may also include identifying a region of the image based on the determined location to insert the content, determining at least one lighting parameter based on the identified region, and rendering the content using the determined at least one lighting parameter.

Abstract: Systems and techniques for multispectral lighting reproduction, in one aspect, include: one or more light sources having different lighting spectra; and one or more computers comprising at least one processor and at least one memory device, the one or more computers programmed to drive the one or more light sources directly using intensity coefficients that have been determined by comparing first data for a multi-color reference object photographed by a camera in a scene with second data for the multi-color reference object photographed when lit by respective ones of the different lighting spectra of the one or more light sources.

Abstract: Systems and methods for lighting inserted content are provided. For example, the inserted content may include augmented reality content that is inserted into an image of a physical space. An example system and method may include determining a location within an image to insert content. For example, the image may be captured by a camera device. The example system and method may also include identifying a region of the image based on the determined location to insert the content, determining at least one lighting parameter based on the identified region, and rendering the content using the determined at least one lighting parameter.

Abstract: Systems and methods for measuring spectra and other optical characteristics such as colors, translucence, gloss, and other characteristics of objects and materials such as skin. Instruments and methods for measuring spectra and other optical characteristics of skin or other translucent or opaque objects utilize an abridged spectrophotometer and improved calibration/normalization methods. Improved linearization methods also are provided, as are improved classifier-based algorithms. User control is provided via a graphical user interface. Product or product formulations to match the measured skin or other object or to transform the skin or other object are provided to lighten, darken, make more uniform and the like.

Abstract: Systems and techniques for multispectral lighting reproduction, in one aspect, include: one or more light sources having different lighting spectra; and one or more computers comprising at least one processor and at least one memory device, the one or more computers programmed to drive the one or more light sources directly using intensity coefficients that have been determined by comparing first data for a multi-color reference object photographed by a camera in a scene with second data for the multi-color reference object photographed when lit by respective ones of the different lighting spectra of the one or more light sources.

Abstract: Systems and methods for measuring spectra and other optical characteristics such as colors, translucence, gloss, and other characteristics of objects and materials such as skin. Instruments and methods for measuring spectra and other optical characteristics of skin or other translucent or opaque objects utilize an abridged spectrophotometer and improved calibration/normalization methods. Improved linearization methods also are provided, as are improved classifier-based algorithms. User control is provided via a graphical user interface. Product or product formulations to match the measured skin or other object or to transform the skin or other object are provided to lighten, darken, make more uniform and the like.