AI models for graphics editing

Below is a list of AI model disciplines that will be useful tools in the graphics editing process. Feel free to comment with ideas for more items missing from this list.

- Image generation (to turn inputs like text, images, masks, or other control data into a desired image)
- Infill (to generate the content in missing areas based on a mask, or uncrop an image)
- Style transfer (to adapt the subject of one image to the art style of another image)
- Upscaling
- Chroma keying ([CorridorKey](https://github.com/nikopueringer/CorridorKey))
- Segmentation (to automatically mask a subject or break a scene apart into multiple subjects)
- Depth estimation (to generate a depth map that can be used for many procedural effects)
- Decomposing into render channels like albedo, normal, depth, irradiance, roughness, metalness ([RGB↔X](https://zheng95z.github.io/publications/rgbx24?lid=7ibrcrhosh7a))
- Relighting (to change the direction and color cast of the lighting on a scene or subject)
- Novel view synthesis (to alter the perspective angle or FoV of a subject)
- Altering a scene's focus or deconvolving blur
- Un-smearing a motion-blurred image
- Noise removal (sensor noise, JPEG artifacts, dust/scratches/damage to physically scanned images)
- Colorizing grayscale images
- SDR to HDR conversion by inferring the extra data that was outside the camera's dynamic range
- Recovering clipped pixels in overexposed scenes
- Color gamut extension by inferring true WCG colors of a scene beyond the range of the imaging device sensor

The issue names no source files, tests, or entry points and only lists possible AI disciplines for graphics editing. First define a concrete model or workflow and the project area it should affect; completion is not currently specified.

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