What Does Temporal Sequence Mean in AI Video Generation?
'Temporal' relates to time
'Sequence' = a series of frames (images) that play one after another to create a video.
In simple terms:
A temporal sequence is the flow of frames across time, i.e. how each frame connects to the one before and after it to create smooth motion.
Why does it matter for Video Generation?
When generating video, the model isn’t just making a series of still images independently of one another.
It has to try and model the relationship between frames over time.
This is what makes a video look fluid and believable, rather than just a fast, randomly moving slideshow.
The model aims to understand:
How objects move naturally
How lighting shifts logically and naturally across scenes
How subjects deform, rotate, or evolve in terms of shape frame by frame
Done effectively, it is known as temporal coherence - making sure time feels continuous and smooth.
If the model loses control of the temporal sequence, you begin to see issues, commonly including:
Jittery motion
Objects melting or flickering
Weird shape distortions
Inconsistent lighting or perspective jumps.
How do seeds relate to temporal sequence?
The seed typically controls the initial state (the first frame/image)
Maintaining temporal sequence over time means managing how that first frame evolves naturally into later frames.
Some models actively guide this process while other models allow for more randomness as subsequent frames unfold, which may allow for greater creative motion but less strict consistency
The temporal sequence is what makes a video feel smooth and connected over time. Seeds can anchor the starting point, but true video consistency also depends on how the AI manages motion frame-to-frame.
If you think of video generation like flipping through a book of drawings, the first drawing (the seed) sets the scene, and the model has to decide how each new page changes to create smooth motion. Managing this sequence well is what makes a video feel real and connected.
How do different models approach seeds?
Runway
Seed behaviour:
Runway introduces randomness between frames.
Even if using a fixed seed, Runway does not 'lock the temporal sequence tightly'.
What this means:
Runway focuses heavily on creative variety and scene evolution, not strict frame-by-frame anchoring.
The seed might influence the overall style, starting point or key features, but not every frame is tightly bound to that seed. There is still randomness or variability in how the motion and continuity unfolds (temporal dynamics).
Longer sequences might start to display slight flickering or shifts in appearance or motion across frames. This happens because the model doesn’t enforce strict frame-to-frame consistency tied to the seed.
Why this matters:
For work focused on creativity and ideation, this might be acceptable or even desirable - allowing some flexibility and variation.
For scenarios that require precise control, like animation workflows, the lack of tightly locked temporal control with seeds may be a limitation.
Veo2
Seed behaviour:
Like in other models, a seed initialises the random process that affects how the video is produced.
It is intended to give you reproducibility - so that using the same prompt and seed ideally yields the same or a very similar video.
What this means:
Veo2 is designed to model temporal consistency with seeds much more explicitly than other models like Runway.
It has a number of advancements over pre-existing models that allow it to maintain better coherence in motion, lighting, and structure over time.
Veo2 has high but not perfect determinism. While it strives for tighter temporal coherence than, say, Runway, it may still have minor variability.
This also means that while Veo2 is closer to deterministic generation than, say, Runway, it may not fully lock the seed to the exact sequence.
Why this matters:
For creatives that require high fidelity, e.g. in storytelling or cinematic shots, Veo2's stronger temporal modelling is a significant advantage, even if it is not absolutely deterministic.
Stable Video
Seed behaviour:
Stable Video takes something more akin to a 'seed + motion control' approach
What this means:
This means the initial frame is seed-locked, and motion is extrapolated with tighter consistency than other models.
Why this matters:
Seed + controlled motion blending can lead to better consistency across frames - especially in short clips.
The model's very limited freedom from the seed, or introduction of randomness, can make it less suited to creative work and ideation.