Adobe, Inc.

Korea Advanced Institute of Science and Technology

Soo Ye Kim

Zhe Lin

Jianming Zhang

Zhihong Ding

Luis Figueroa

Mun Churl Kim

Simon Niklaus

Su Chen

Dae Eun Kim

Yifei Fan

The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides a graphical user interface experience to move objects and generate new shadows within a digital image scene. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems receive a selection to position an object in a first location within the scene. Further, the disclosed systems composite an image by placing the object at the first location within the scene of the digital image. Moreover, the disclosed systems generate a modified digital image having a shadow of the object where the shadow is consistent with the scene and provides the modified digital image to the client device.

Moving objects casting a shadow and generating proxy shadows within a digital image

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to remove a shadow for an object. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access a shadow mask of the shadow in a first location. Further, the disclosed systems generate the modified digital image without the shadow by generating a fill for the first location that preserves a visible location of the first location. Moreover, the disclosed systems generate the digital image without the shadow for the object by combining the fill with the digital image.

Texture-preserving shadow removal in digital images utilizing generating inpainting models

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate refined depth maps of digital images utilizing digital segmentation masks. In particular, in one or more embodiments, the disclosed systems generate a depth map for a digital image utilizing a depth estimation machine learning model, determine a digital segmentation mask for the digital image, and generate a refined depth map from the depth map and the digital segmentation mask utilizing a depth refinement machine learning model. In some embodiments, the disclosed systems generate first and second intermediate depth maps using the digital segmentation mask and an inverse digital segmentation mask and merger the first and second intermediate depth maps to generate the refined depth map.

Utilizing machine learning models to generate refined depth maps with segmentation mask guidance

In this invention, we propose a convolutional neural network (CNN) based architecture designed for the ITM to HDR consumer displays, called ITM-CNN, and its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. To the best of our knowledge, this invention first presents the ITM problem using CNNs for HDR consumer displays, where the network is trained to restore lost details and local contrast. Our ITM-CNN can readily up-convert LDR images for direct viewing on an HDR consumer medium, and is a very powerful means to solve the lack of HDR video contents with legacy LDR videos.

Method and apparatus for inverse tone mapping

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Daejeon, South Korea

Soo Ye Kim