Visual artifacts are unintended errors or imperfections that appear in AI-generated images or videos as a result of the generation process. They can take many forms, including blurry patches, distorted edges, repeated patterns, misshapen hands, flickering pixels, or strange blending between objects that should be visually distinct.

Artifacts occur when an AI model does not have enough information, processing capacity, or training examples to accurately render a particular part of a scene. Common causes include low inference steps, aggressive compression, conflicting prompt instructions, or the model operating at the edge of its capabilities. Certain subjects, such as human hands, text, and complex backgrounds, are historically more prone to artifacts than others.

Reducing artifacts is a key goal in the ongoing development of AI video and image generation tools. Higher-quality and more recent model releases generally produce significantly fewer artifacts than earlier generations, as training improvements and architectural advances address the root causes of common failure modes. When artifacts do appear, techniques like inpainting, upscaling, and iterative refinement can help clean up or replace problem areas in generated content.