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

L’issue ne nomme aucun fichier source, test ou point d’entrée et se contente de lister de possibles disciplines d’IA pour l’édition graphique. Commencez par définir un modèle ou un workflow concret ainsi que la partie du projet qu’il devrait affecter ; les critères d’achèvement ne sont actuellement pas spécifiés.

Rédigé par le modèle d'indexation à partir du texte de l'issue.