BACKGROUND

Digital imagery systems often portray digitally enhanced images or other scenes with visual effects. For example, some existing digital imagery systems render both real objects and computer-simulated objects in scenes that include lighting from light sources either within or without a field of view for digital images. Some digital imagery systems adjust the color, intensity, or other features of lighting from multiple light sources. When a digital image includes lighting from multiple light sources, a digital imagery system may need to solve complex problems to reflect spatially varying lighting from multiple sources—particularly for indoor digital images. Despite making significant advances, existing digital imagery systems exhibit limitations that inhibit such systems from quickly rendering objects at positions, accurately and realistically rendering objects with spatially varying lighting, and training a neural network to generate spatially varying lighting parameters.

To render digital images with lighting for a computer-simulated object, for example, some conventional digital imagery systems recover geometry, reflectance, and illumination from a single digital image of an arbitrary object using hand-crafted priors for components of the digital image. In some cases, conventional digital imagery systems assume the geometry of indoor scenes based on known geometries. But both hand-crafted priors and assumed geometry often result in lighting parameters that unrealistically portray lighting conditions of computer-simulated objects in a digital image, particularly for scenes with light from multiple sources. Such systems often cannot illuminate virtual objects with lighting that matches that of a real physical scene.

In addition to unrealistic portrayals of lighting, some existing digital imagery systems solve complex computing problems or use overly complicated network architectures that slow down the output of lighting parameters. In some cases, for instance, existing digital imagery systems reconstruct a multi-view three-dimensional model of a digital image's geometry as a basis for generating lighting parameters. Further, some existing digital imagery systems apply a rendering-based optimization to estimate scene geometry and reflectance, detect light-source positions, and estimate light source intensities—but only by applying challenging inverse computing equations solved with heuristics.

Both reconstruction of a scene's geometry and computationally heavy inverse equations slow down the computational time in which such systems generate lighting parameters. As objects move or lighting changes within a physical environment, such digital imagery systems accordingly consume excessive time and user input to portray lighting for virtual objects in different positions or different lighting conditions.

SUMMARY

This disclosure describes embodiments of methods, non-transitory computer readable media, and systems that solve the foregoing problems and provide other benefits. For example, the disclosed systems can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.

In some embodiments, for example, the disclosed systems identify a request to render a virtual object at a designated position within a digital image. The disclosed systems subsequently extract a common feature vector from the digital image utilizing common network layers of a source-specific-lighting-estimation-neural network. Based on the common feature vector, the systems further generate 3D-source-specific-lighting parameters utilizing parametric-specific-network layers of the source-specific-lighting-estimation-neural network. In response to the request to render, the systems accordingly render a modified digital image comprising the virtual object at the designated position illuminated according to the 3D-source-specific-lighting parameters.

The following description sets forth additional features and advantages of the disclosed methods, non-transitory computer readable media, and systems, and may make such additional features and advantages obvious or disclose them from the practice of exemplary embodiments.

