What is temporal consistency?
A video is not a collection of independent images. It is a sequence where each frame must be coherent with the one before it and the one after it. Temporal consistency is the property that makes that coherence hold.
Definition
Temporal consistency describes the degree to which visual elements remain stable and coherent across frames in a video. A temporally consistent video has objects that do not shift position unnaturally between frames, lighting that does not flicker, colors that do not drift, and motion that looks physically plausible.
The opposite, temporal inconsistency, produces videos that feel visually unstable: textures that swim across surfaces, characters whose faces change subtly from frame to frame, or backgrounds that shift between cuts.
Why temporal consistency is the central challenge of video generation
Generating a single high-quality image is a solved problem. Generating a sequence of high-quality images that looks like a coherent video is not.
The challenge is that generating each frame independently, even from the same prompt and the same model, produces different outputs. Without explicit mechanisms to enforce consistency across frames, a naive video generation system produces something closer to a slideshow than a video.
Every major architectural decision in modern video generation models traces back to this problem. Temporal attention, spatiotemporal modeling, the use of 3D convolutions, conditioning on previous frames: all of these are responses to the same challenge. How do you make generated frames cohere over time?
What temporal consistency covers
Object consistency: A character's face, clothing, and physical properties should remain stable across shots. Textures on objects should not shift or swim.
Lighting consistency: Light sources, shadows, and reflections should behave consistently with a coherent scene setup.
Color consistency: Palette and color grading should hold without drifting.
Motion consistency: Movement should follow physically plausible trajectories. Objects should not teleport between frames. Camera motion should be smooth where intended.
Identity preservation: For characters and specific subjects, identity should be maintained even through motion, partial occlusion, or changing angles.
How video models achieve temporal consistency
Temporal attention allows the model to attend across frames during generation, letting each frame's generation be informed by adjacent frames. LTX-2's spatiotemporal transformer attends across both spatial dimensions within frames and temporal dimensions across frames simultaneously.
Conditioning on previous frames gives the model explicit information about what the prior frame looked like, anchoring subsequent frames to it.
Optical flow guidance provides explicit motion vectors that constrain where objects should be in each subsequent frame given their position and velocity in the current one.
Latent space consistency helps because operating in a compressed latent space smooths out high-frequency per-frame variation that would otherwise produce flickering in pixel space.
Evaluation
Temporal consistency is typically evaluated using metrics that measure feature similarity between adjacent or non-adjacent frames, as well as perceptual metrics that assess smoothness of motion and stability of objects.
Common tools include CLIP-based consistency scores, optical flow smoothness measures, and human preference evaluations where raters compare outputs on perceived stability and naturalness of motion.
How LTX-2 addresses temporal consistency
LTX-2.3 uses a spatiotemporal transformer architecture that reasons about frames jointly rather than independently. The January 2026 training improvements specifically targeted two common consistency failures: frozen videos (clips that barely move) and the "Ken Burns effect" (unintentional slow zoom or pan driven by the model defaulting to simple motion patterns).
The result is better object consistency, smoother motion, fewer cuts, and more physically coherent dynamics across clips. For developers evaluating generation quality, temporal consistency is one of the primary axes on the LTX-2 model benchmarks.
