Temporal Consistency and Flicker in AI Video

Flicker first became a widely known problem when people made video with image generators. If you denoise each frame separately, every frame starts from its own random noise and makes its own small choices about texture, hair strands or the pattern on a shirt. Each frame looks fine alone, but played in sequence those independent choices read as shimmer. Modern video generators, including Stable Video Diffusion (2023), OpenAI's Sora (previewed in 2024), Runway's Gen-3 and Google's Veo models, generate the frames of a clip together in a compressed latent space and use attention across time, so each frame is shaped by the others. That is the main reason recent clips are much steadier than early experiments. Drift still happens, for several reasons. Fine, high-frequency detail such as hair, foliage, water and on-screen text is hardest, because tiny errors in small details show up as sparkle. When something is hidden behind another object, the model has to carry its appearance through the gap, and it may bring it back slightly different. Longer videos are often built by extending or stitching shorter clips, and because the model sees only a limited window of recent frames, identity can wander over time. Compression in the video autoencoder can also add artifacts where chunks join. There are a few common misconceptions. Flicker is not mainly a frame-rate problem, and raising resolution does not fix it by itself. Post-processing can smooth brightness flicker, but it cannot restore a character's face once it has changed. Frame interpolation can make motion look smoother, but it can also smear drift across the new frames. Real fixes happen during generation: temporal attention, reference-image conditioning, and propagating information from earlier frames into later ones.