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)
• 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)
• 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