Key takeaways
- AI denoising can clean noisy renders and help artists evaluate images sooner.
- AI rotoscoping can reduce repetitive masking work across video frames.
- Background removal tools can create useful starting masks for compositing.
- AI upscaling can upscale old assets and low resolutions.
- Content Aware Fill can help remove unwanted elements using surrounding image information.
- AI tools still require artists to review quality and correct mistakes.
TL;DR
AI has been part of creative software for longer than the current wave of image generation might suggest. Denoising, rotoscoping, background removal, Content Aware Fill, and image upscaling all use forms of intelligent processing to reduce repetitive or computationally demanding work. For artists, these tools are often most useful when they make an existing workflow faster while leaving decisions about composition, lighting, masking, retouching, and final quality in the artist's hands. These AI tools also do not remove rendering workload, and artists still need to find ways to optimize their scenes and assets, or use render farms to speed up rendering.
AI tools are already part of creative workflows
AI in creative software does not always begin with a text prompt. Artists can encounter machine learning while rendering a 3D scene, selecting a subject in Photoshop, separating a person from a background, cleaning unwanted objects from footage, or preparing an image for compositing.
These tools generally work with material that already exists, analyzing pixels, movement, edges, or rendered information to automate repetitive tasks such as masking, cleanup, object removal, and render previewing. This can speed up parts of the workflow while still leaving the creative decisions and final edits in the artist’s hands.
How AI denoising helps with 3D rendering
Noise is a familiar problem in ray traced rendering. When a renderer has not calculated enough samples, areas with complex lighting, reflections, shadows, or indirect illumination can appear grainy. Increasing the number of samples can produce a cleaner result, but it also requires more rendering work. AI denoisers analyze a noisy render and attempt to reconstruct a cleaner image from the available information.
Faster feedback and lesser costs
Denoising can be particularly helpful during look development. An artist adjusting lights, materials, cameras, or reflections may not need a completely finished render every time something changes. A denoised preview can make the image easier to judge while the scene is still being developed. It’s also particularly useful for rendering, and can cut costs and time by rendering a noisy image and denoising it after.
Denoising has limits
A denoiser still has to work with the information available in the render. Very low sample counts can leave too little reliable detail, while difficult reflections, fine textures, small highlights, and other complex features may be altered during cleanup.
AI rotoscoping can reduce repetitive masking
Rotoscoping traditionally requires an artist to create a mask around a subject and adjust it as the subject moves through a shot. Even a relatively simple shape can become tedious when its position, silhouette, or visibility changes across many frames. AI assisted rotoscoping can create an initial separation and propagate it through footage.
Difficult edges still need attention
Hair, fur, transparent materials, motion blur, overlapping objects, and similar foreground and background colors can make isolation difficult. Even Adobe's Roto Brush workflow includes dedicated refinement controls for detailed edges such as hair and for areas affected by motion blur.
For professional compositing, those small edge decisions matter. AI can establish a useful matte quickly, but the artist still needs to decide where the boundary belongs and whether it holds up throughout the shot.
Background removal uses the same basic idea
Automatic background removal is closely connected to rotoscoping and subject selection. The software has to identify the foreground subject, separate it from everything behind it, and create an edge that can be used as a mask.
For a still image, this can turn a time consuming manual selection into a much faster starting point. For moving footage, the challenge becomes more complicated because the mask has to remain believable as the subject and camera move.
One click removal is rarely the whole workflow
Simple backgrounds and clearly defined subjects can produce convincing automatic results. Fine hair, glass, soft shadows, blurred edges, and partially transparent objects are harder to separate cleanly. That is why editable masks are valuable. An automatic result can handle the broad isolation first, while manual tools remain available for the details that need more control.
AI upscaling can improve image resolution
AI upscaling is another way machine learning can support an existing creative workflow. Instead of generating a completely new image, an upscaler analyzes a lower resolution image and increases its size while trying to preserve edges, textures, and other visual details.
This can be useful for enlarging older assets, increasing the resolution of renders, preparing images for larger displays, or improving source material that was created at a smaller size. The quality of the result still depends heavily on how much useful detail exists in the original image.
How AI upscaling works
Traditional resizing methods mainly calculate new pixels from the pixels already present. AI upscaling uses models trained to recognize visual patterns and estimate how details may appear at a higher resolution, which can produce sharper edges and more convincing textures in some images.
The process can still introduce artifacts, overly smooth surfaces, or details that do not accurately match the source. Artists should check important textures, faces, text, fine patterns, and rendered details closely before using an upscaled image as a final output.
Content Aware Fill is another useful AI tool
Content Aware Fill has been around long enough that it may not immediately come to mind when people discuss AI tools for artists. Adobe has documented the Photoshop feature as using its artificial intelligence and machine learning technology to analyze nearby colors and textures when filling a selected area.
The basic workflow is straightforward. The artist selects an unwanted object, opens Content Aware Fill, and Photoshop uses surrounding pixels to replace the selected area. The sampling region can also be adjusted so the artist has some control over which parts of the image contribute to the fill.
Useful for cleanup and retouching
Content Aware Fill can help remove small objects, distractions, imperfections, or other unwanted elements from an image. It is especially convenient when the surrounding area contains enough similar texture or color for Photoshop to build a convincing replacement. However, complex scenes can still require additional work. Repeating patterns, strong perspective, shadows, intersecting objects, or several different textures may cause obvious errors. In those situations, changing the sampling area or combining Content Aware Fill with manual retouching can give the artist more control.
Content Aware Fill also works with video
After Effects includes its own Content Aware Fill workflow for removing unwanted objects from footage. Adobe describes the feature as analyzing frames over time so that it can synthesize replacement pixels using information from other frames. That temporal element makes video cleanup different from filling a single photograph. The replacement has to remain convincing as the footage changes, which means artists still need to check the entire shot rather than judging the result from one frame.
Where AI fits into general rendering
Denoising is the clearest example of AI being used directly around rendering, but intelligent processing can also appear later in the pipeline. Upscaling can help increase the usable resolution of certain images or footage, while object detection and masking can make rendered elements easier to integrate into a larger composite.
AI does not remove the rendering workload
AI denoising can reduce the number of samples needed in some situations, but geometry, textures, lighting, simulations, resolution, and animation length still affect render times. Larger projects still need sufficient CPU or GPU resources, and artists can use a render farm when local hardware becomes the main bottleneck.
When manual tools are still the better choice
Automation is useful when it produces a result that requires less work than doing the task manually. If an automatic mask needs constant corrections or a denoised render loses important details, the faster method can quickly become the slower one.
Traditional tools such as manual masks, higher render samples, cloning, careful retouching, and conventional compositing remain valuable because they give artists direct control over the result. The best method depends on the shot, image, deadline, and level of precision required.
Understanding the process still matters
Artists who understand rendering noise, masks, rotoscoping, Content Aware Fill, and upscaling can more easily spot when an automated result is inaccurate or being pushed too far. Knowing what a good result should look like also makes it easier to decide when AI has worked, when it needs refinement, and when a manual method will be more reliable.
Final thoughts
AI tools for artists can be surprisingly ordinary. Denoising a noisy render, tracking a subject, removing a background, selecting an object, filling an unwanted area, and upscaling an image are all practical tasks where intelligent processing can reduce repetitive work.
The value comes from using these tools selectively. When AI can create a useful starting point, shorten an expensive process, or handle repetitive cleanup, it can leave more time for the parts of the project that need artistic judgment. The artist still decides whether the result is accurate, whether the quality is good enough, and what ultimately belongs in the finished work.
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