A tool allows a user to create new designs for apparel and preview these designs in three dimensions before manufacture. Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. Based on a laser input file with a pattern, a laser will burn the pattern onto apparel. With the tool, the user will be able to create, make changes, and view images of a design, in real time, before burning by a laser. Input to the tool includes fabric template images, laser input files, and damage input. The tool allows adding of tinting and adjusting of intensity and bright point. The user can also move, rotate, scale, and warp the image input.

A tool allows a user to create new designs for apparel and preview these designs in three dimensions before manufacture. Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. Based on a laser input file with a pattern, a laser will burn the pattern onto apparel. With the tool, the user will be able to create, make changes, and view images of a design, in real time, before burning by a laser. Input to the tool includes fabric template images, laser input files, and damage input. The tool allows adding of tinting and adjusting of intensity and bright point. The user can also move, rotate, scale, and warp the image input.

In examples, a filter used to denoise shadows for a pixel(s) may be adapted based at least on variance in temporally accumulated ray-traced samples. A range of filter values for a spatiotemporal filter may be defined based on the variance and used to exclude temporal ray-traced samples that are outside of the range. Data used to compute a first moment of a distribution used to compute variance may be used to compute a second moment of the distribution. For binary signals, such as visibility, the first moment (e.g., accumulated mean) may be equivalent to a second moment (e.g., the mean squared). In further respects, spatial filtering of a pixel(s) may be skipped based on comparing the mean of variance of the pixel(s) to one or more thresholds and based on the accumulated number of values for the pixel.

In examples, a filter used to denoise shadows for a pixel(s) may be adapted based at least on variance in temporally accumulated ray-traced samples. A range of filter values for a spatiotemporal filter may be defined based on the variance and used to exclude temporal ray-traced samples that are outside of the range. Data used to compute a first moment of a distribution used to compute variance may be used to compute a second moment of the distribution. For binary signals, such as visibility, the first moment (e.g., accumulated mean) may be equivalent to a second moment (e.g., the mean squared). In further respects, spatial filtering of a pixel(s) may be skipped based on comparing the mean of variance of the pixel(s) to one or more thresholds and based on the accumulated number of values for the pixel.

A free viewpoint video generation unit (24) (generation unit) of an information processing apparatus (10a) generates a free viewpoint video (J) for viewing a 3D model (90M) (3D object) superimposed on background information (92) from an arbitrary viewpoint position. Then, a shadow application unit (27) generates a shadow (94) of a light source generated on the 3D model (90M) according to the viewpoint position based on light source information (93) indicating the position of the light source related to the background information (92) and the direction of the light beam emitted by the light source, depth information (D) (three-dimensional information) of the 3D model (90M), and the viewpoint position, and applies the generated shadow to the free viewpoint video (J).

A free viewpoint video generation unit (24) (generation unit) of an information processing apparatus (10a) generates a free viewpoint video (J) for viewing a 3D model (90M) (3D object) superimposed on background information (92) from an arbitrary viewpoint position. Then, a shadow application unit (27) generates a shadow (94) of a light source generated on the 3D model (90M) according to the viewpoint position based on light source information (93) indicating the position of the light source related to the background information (92) and the direction of the light beam emitted by the light source, depth information (D) (three-dimensional information) of the 3D model (90M), and the viewpoint position, and applies the generated shadow to the free viewpoint video (J).

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a height map for a digital object portrayed in a digital image and further utilizes the height map to generate a shadow for the digital object. Indeed, in one or more embodiments, the disclosed systems generate (e.g., utilizing a neural network) a height map that indicates the pixels heights for pixels of a digital object portrayed in a digital image. The disclosed systems utilize the pixel heights, along with lighting information for the digital image, to determine how the pixels of the digital image project to create a shadow for the digital object. Further, in some implementations, the disclosed systems utilize the determined shadow projections to generate (e.g., utilizing another neural network) a soft shadow for the digital object. Accordingly, in some cases, the disclosed systems modify the digital image to include the shadow.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a height map for a digital object portrayed in a digital image and further utilizes the height map to generate a shadow for the digital object. Indeed, in one or more embodiments, the disclosed systems generate (e.g., utilizing a neural network) a height map that indicates the pixels heights for pixels of a digital object portrayed in a digital image. The disclosed systems utilize the pixel heights, along with lighting information for the digital image, to determine how the pixels of the digital image project to create a shadow for the digital object. Further, in some implementations, the disclosed systems utilize the determined shadow projections to generate (e.g., utilizing another neural network) a soft shadow for the digital object. Accordingly, in some cases, the disclosed systems modify the digital image to include the shadow.

A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.

A method of rendering an image of an environment is disclosed. Environment data for the environment is accessed. The environment data corresponds to a frame of a video. A plurality of subframes associated with the frame is determined. An angle for each of the plurality of subframes is determined. One or more lights corresponding to the environment are selected. For each light of the one or more lights, a shadow map is generated. The shadow map corresponds to a subframe of the plurality of subframes based on a frustum view oriented at the angle determined for the subframe. The image of the environment is rendered. The rendering includes using the generated shadow map for each light of the one or more lights.