Granted Patent B2

US 12,626,429 · App. 18/651,376 · Granted May 12, 2026

Inventors: Tianyu Wang (Hong Kong, CN); Soo Ye Kim (Daejeon, KR); Luis Figueroa (San Jose, CA); Haitian Zheng (Rochester, NY); Jianming Zhang (Campbell, CA); Zhihong Ding (Fremont, CA); Scott Cohen (Sunnyvale, CA); Zhe Lin (Fremont, CA); Wei Xiong (San Jose, CA)

Assignee: Adobe Inc.

G06T11/40G06T5/60G06T5/77G06T2200/24G06T2207/20084

Abstract

Claims (66)

1 . A computer-implemented method comprising:

receiving a digital image depicting a scene comprising an object and a shadow of the object;

accessing an object mask of the object in the digital image; and

generating a modified digital image by:

extracting, from a combination of the digital image and the object mask, utilizing an encoder of a shadow analyzer model, multi-scale features, global features, and spatial features;

generating, from the global features and the spatial features, utilizing a global-spatial decoder, the modified digital image without the shadow of the object; and

generating, from the multi-scale features from a spatial decoder of the global-spatial decoder, utilizing a shadow detector, a shadow mask of the shadow.

2 . The computer-implemented method of claim 1 , wherein generating the modified digital image comprises:

receiving, via a user interaction with the digital image an indication to remove the shadow of the object; and

generating a fill corresponding to the removed shadow of the object that is consistent with the digital image based on the global features and the spatial features.

3 . The computer-implemented method of claim 1 , wherein accessing the object mask further comprises receiving lighting data and geometry of the scene to generate pixels for the shadow removed from digital image that is globally and locally consistent with a remainder of the modified digital image.

4 . The computer-implemented method of claim 1 , wherein generating the modified digital image without the shadow of the object comprises generating, utilizing a generative inpainting neural network of the shadow analyzer model, pixel values consistent with the scene and without the shadow by modulating the generative inpainting neural network based on the object mask of the object.

5 . The computer-implemented method of claim 1 , wherein generating the shadow mask of the shadow comprises:

utilizing a shadow detector integrated with the global-spatial decoder to identify a shadow region of the digital image by upsampling multi-scale features to a uniform size; and

combining the uniform size of the upsampled multi-scale features to a feature map to generate the shadow mask of the shadow.

6 . The computer-implemented method of claim 1 , further comprising:

receiving an additional digital image with a plurality of objects and a plurality of shadows corresponding to the plurality of objects; and

accessing an empty mask for the additional digital image.

7 . The computer-implemented method of claim 6 , further comprising in response to receiving the empty mask, removing the plurality of shadows corresponding to the plurality of objects in the additional digital image.

8 . The computer-implemented method of claim 1 , further comprising:

receiving an additional digital image comprising an additional shadow cast from an additional object outside of a frame of the additional digital image;

accessing an empty mask in response to identifying that the additional shadow is cast from the additional object outside of the frame; and

in response to the empty mask, removing the additional shadow from the additional digital image.

9 . A non-transitory computer-readable medium storing instructions thereon that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

in response to receiving a user interaction to modify an object depicted in a digital image, accessing an object mask of the object;

combining the object mask of the object and the digital image to generate a combined representation;

receiving, from a shadow analyzer model, a shadow mask of the object;

generating, from the combined representation and utilizing a shadow synthesis diffusion model, a new shadow for the object by conditioning the shadow synthesis diffusion model with the shadow mask of the object received from the shadow analyzer model; and

generating a modified digital image that includes the object with the new shadow.

10 . The non-transitory computer-readable medium of claim 9 , wherein:

receiving the user interaction to modify the object comprises relocating the object by moving the object in the digital image from a first location in the digital image to a second location in the digital image; and

combining the object mask of the object and the digital image comprises combining the object mask and the digital image depicting the object moved to the second location to generate the combined representation.

11 . The non-transitory computer-readable medium of claim 9 , wherein:

receiving the user interaction to modify the object comprises relocating the object by adding the object to a location of the digital image, the object coming from an additional digital image; and

combining the object mask of the object and the digital image comprises combining the object mask and the digital image depicting the added object in the location to generate the combined representation.

12 . The non-transitory computer-readable medium of claim 9 , further comprising:

accessing, from the shadow analyzer model, shadow property data comprising intensity, softness, color, and direction of a shadow corresponding to the shadow mask of the object; and

combining the shadow property data to generate a feature map of the shadow mask.

13 . The non-transitory computer-readable medium of claim 12 , wherein the operations further comprise resizing the feature map of the shadow mask from a first size to a second size, the second size being smaller than the first size.

14 . The non-transitory computer-readable medium of claim 13 , wherein conditioning the shadow synthesis diffusion model with the shadow mask of the object comprises:

utilizing an adapter of the shadow synthesis diffusion model to align the resized feature map with text tokens; and

utilizing a cross-attention mechanism of the shadow synthesis diffusion model to condition an iterative denoising process of the shadow synthesis diffusion model with the resized feature map aligned with the text tokens.

15 . The non-transitory computer-readable medium of claim 9 , wherein generating the new shadow for the object comprises:

generating, at a first denoising process of the shadow synthesis diffusion model, a first additional shadow mask of the object based on conditioning the first denoising process with the shadow mask of the object;

generating, at a second denoising process of the shadow synthesis diffusion model, a second additional shadow mask of the object based on conditioning the second denoising process with the shadow mask of the object; and

generating the new shadow for the object based on the first additional shadow mask and the second additional shadow mask.

16 . A system comprising:

one or more memory devices; and

one or more processors coupled to the one or more memory devices that cause the system to perform operations comprising:

accessing an object mask of an object and a digital image depicting a scene depicting the object and a shadow of the object;

performing object-centered shadow detection and object-centered removal to generate a modified digital image without the shadow of the object by extracting, via an encoder of a shadow analyzer model, features from a combination of the digital image and the object mask;

in response to receiving a user interaction to manipulate the object, combining the object mask of the object and the modified digital image to generate a combined representation; and

generating, from the combined representation and utilizing a shadow synthesis diffusion model, a modified shadow for the object responsive to the user interaction by conditioning the shadow synthesis diffusion model with a shadow mask of the object generated by the shadow analyzer model.

17 . The system of claim 16 , wherein the operations further comprise:

extracting the features from the combination of the digital image and the object mask by utilizing an encoder of the shadow analyzer model to extract multi-scale features, global features, and spatial features; and

generating, utilizing a shadow detector of the shadow analyzer model, the shadow mask of the object from the multi-scale features.

18 . The system of claim 16 , wherein receiving the user interaction to manipulate the object comprises:

moving the object in the digital image from a first location in the digital image to a second location in the digital image; or

adding the object to a location in an additional digital image.

19 . The system of claim 16 , wherein conditioning the shadow synthesis diffusion model with the shadow mask of the object comprises:

accessing shadow property data comprising intensity, softness, color, and direction of a shadow corresponding to the shadow of the object to generate a feature map of the shadow;

aligning the feature map with text tokens by utilizing an adapter of the shadow synthesis diffusion model to generate shadow embeddings of the shadow mask; and

conditioning the shadow synthesis diffusion model with the shadow embeddings of the shadow.

20 . The system of claim 16 , wherein the operations further comprise:

adding an additional object to the modified digital image, wherein the additional object comes from an additional digital image without a corresponding shadow; and

accessing the modified shadow as a reference shadow to generate a new shadow for the additional object.

Assignments (1)

Continuity (1)

References Cited (67)