SYSTEMS AND METHODS FOR USING AI TO FACILITATE IMAGE EDITING
In some implementations, the techniques described herein relate to a method including: (i) identifying, by a processor, an image. (ii) receiving, by the processor, natural language instructions for editing the image, the natural language instructions including a location within the image and an editing instruction, (iii) editing, by a machine learning model executed by the processor, the location within the image based on the natural language instructions by (a) identifying a region within the image that corresponds to the location in the natural language instructions and (b) editing the identified region by applying the editing instruction to the identified region to generate an edited image, and (iv) causing, by the processor, display of the edited image.
1 . A method comprising:
identifying, by a processor, an image;
receiving, by the processor, natural language instructions for editing the image, the natural language instructions including a location within the image and an editing instruction;
editing, by a machine learning model executed by the processor, the location within the image based on the natural language instructions by:
identifying a region within the image that corresponds to the location in the natural language instructions; and
editing the identified region by applying the editing instruction to the identified region to generate an edited image; and
causing, by the processor, display of the edited image.
2 . The method of claim 1 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises identifying a landmark location within the image that is described by the natural language instructions.
3 . The method of claim 1 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a set of objects of a similar type depicted within the image; and
identifying, based on the natural language instructions, a specific object within the set of objects referred to by the natural language instructions.
4 . The method of claim 1 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a type of object described by the natural language instructions; and
locating an object of the type within the image.
5 . The method of claim 1 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a relative directional descriptor within the natural language instructions; and
identifying the region at least in part based on the relative directional descriptor.
6 . The method of claim 1 , further comprising training the machine learning model by:
identifying a set of triplets that each comprise:
an unmodified version of a training image;
text that comprises a description of a location within the unmodified version of the training image; and
a modified version of the training image that comprises a modification to the location described within the text; and
providing the set of triplets to the machine learning model as input data.
7 . The method of claim 1 , wherein the image comprises a frame of a video.
8 . The method of claim 7 , wherein editing, by the machine learning model executed by the processor, the location within the image based on the natural language instructions comprises editing consecutive frames of the video.
9 . A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
identifying, by a processor, an image;
receiving, by the processor, natural language instructions for editing the image, the natural language instructions including a location within the image and an editing instruction;
editing, by a machine learning model executed by the processor, the location within the image based on the natural language instructions by:
identifying a region within the image that corresponds to the location in the natural language instructions; and
editing the identified region by applying the editing instruction to the identified region to generate an edited image; and
causing, by the processor, display of the edited image.
10 . The non-transitory computer-readable storage medium of claim 9 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises identifying a landmark location within the image that is described by the natural language instructions.
11 . The non-transitory computer-readable storage medium of claim 9 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a set of objects of a similar type depicted within the image; and
identifying, based on the natural language instructions, a specific object within the set of objects referred to by the natural language instructions.
12 . The non-transitory computer-readable storage medium of claim 9 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a type of object described by the natural language instructions; and
locating an object of the type within the image.
13 . The non-transitory computer-readable storage medium of claim 9 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a relative directional descriptor within the natural language instructions; and
identifying the region at least in part based on the relative directional descriptor.
14 . The non-transitory computer-readable storage medium of claim 9 , further comprising training the machine learning model by:
identifying a set of triplets that each comprise:
an unmodified version of a training image;
text that comprises a description of a location within the unmodified version of the training image; and
a modified version of the training image that comprises a modification to the location described within the text; and
providing the set of triplets to the machine learning model as input data.
15 . The non-transitory computer-readable storage medium of claim 9 , wherein the image comprises a frame of a video.
16 . The non-transitory computer-readable storage medium of claim 15 , wherein editing, by the machine learning model executed by the processor, the location within the image based on the natural language instructions comprises editing consecutive frames of the video.
17 . A device comprising:
a processor; and
a storage medium for tangibly storing thereon logic for execution by the processor, the logic comprising instructions for:
identifying, by a processor, an image;
receiving, by the processor, natural language instructions for editing the image, the natural language instructions including a location within the image and an editing instruction;
editing, by a machine learning model executed by the processor, the location within the image based on the natural language instructions by:
identifying a region within the image that corresponds to the location in the natural language instructions; and
editing the identified region by applying the editing instruction to the identified region to generate an edited image; and
causing, by the processor, display of the edited image.
18 . The device of claim 17 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises identifying a landmark location within the image that is described by the natural language instructions.
19 . The device of claim 17 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a set of objects of a similar type depicted within the image; and
identifying, based on the natural language instructions, a specific object within the set of objects referred to by the natural language instructions.
20 . The device of claim 17 , wherein identifying the region within the image that corresponds to the location in the natural language instructions comprises:
identifying a type of object described by the natural language instructions; and
locating an object of the type within the image.