How Does AI Video Generation Work? Text-to-Video vs Image-to-Video

AI video generation looks deceptively simple.

You type:

A golden retriever runs through shallow ocean water at sunset.
The camera tracks alongside the dog in slow motion.

and a model generates several seconds of moving video.

Or you upload a photograph and request:

The woman turns toward the camera while her hair moves gently in the wind.

The image begins to move.

Underneath that simple interface is a much harder problem than generating a single image.

An image generator needs to create one visually convincing frame.

A video generator must create many frames that are not only individually convincing but also consistent with one another across time.

The basic challenge is:

good image
+
good next image
+
good next image
+
consistent motion
+
consistent objects
+
consistent camera
+
consistent lighting
=
good video

Modern video-generation systems solve this with architectures that jointly model visual appearance and time.

This guide explains the major concepts behind text-to-video and image-to-video generation without assuming that every current system uses exactly the same model architecture.

What is AI video generation?

AI video generation uses a trained generative model to create a sequence of visual frames.

The input may include:

text
image
video
mask
pose
camera instructions
other conditioning

The output is generally:

frame 1
frame 2
frame 3
...
frame N

played rapidly enough to appear as continuous motion.

For example:

16 frames per second
×
5 seconds
=
80 frames

Those frames cannot simply be unrelated images.

They need temporal consistency.

Why video generation is harder than image generation

Suppose an image model generates this perfectly:

Frame 1:
a man wearing a blue jacket
standing beside a red car

Now generate the next frame independently.

The new image might contain:

a slightly different face
different jacket
different car shape
different background
different lighting

Each image may look good by itself.

Together they look terrible as video.

The problem is time.

A video model must understand that many visual properties should persist from one frame to the next.

Examples include:

identity
clothing
object shape
background
lighting
camera position
geometry
motion direction

while other things should change smoothly:

body pose
facial expression
object position
hair
water
smoke
camera movement

This is known broadly as temporal consistency.

What is temporal consistency?

Temporal consistency means that visual information evolves coherently across frames.

Imagine a five-second clip of a red car driving past the camera.

Good temporal consistency means:

Frame 1: red car
Frame 2: same red car, slightly farther forward
Frame 3: same red car
Frame 4: same car continues moving
Frame 5: same car exits the shot

Poor consistency might look like:

Frame 1: red sedan
Frame 2: slightly different sedan
Frame 3: headlights change
Frame 4: wheel geometry changes
Frame 5: vehicle becomes an SUV

Humans notice these errors immediately.

Our perception is extremely sensitive to motion and identity changes across adjacent frames.

Text-to-video vs image-to-video

The two most common generation modes are:

Text-to-Video
T2V

and:

Image-to-Video
I2V

They solve related but different problems.

What is text-to-video?

Text-to-video starts from a text description.

For example:

A small sailboat crossing a dark lake during a thunderstorm,
cinematic wide shot, lightning in the distance.

The model needs to determine almost everything:

boat appearance
lake
weather
lighting
camera
composition
motion
duration
frame-to-frame evolution

There is no starting image defining the scene.

Conceptually:

Text prompt
     ↓
Text encoder
     ↓
Text conditioning
     ↓
Video generation model
     ↓
Video latent / frames
     ↓
Decoder
     ↓
Video

Text-to-video provides maximum creative freedom but also gives the model many decisions to make.

What is image-to-video?

Image-to-video starts with an image.

For example:

starting image
+
"The child looks toward the lake as the camera slowly moves closer."

The input image already defines much of the first-frame appearance:

person
face
clothing
background
composition
colors
lighting
camera angle

The video model mainly needs to determine how the scene evolves over time.

Conceptually:

Starting image
      ↓
Image conditioning
      ↓
Motion / text instructions
      ↓
Video model
      ↓
Future temporal states
      ↓
Video

This often gives the creator much more control over character appearance and initial composition.

Why image-to-video is useful

Suppose you need a specific character.

Text-to-video might generate a slightly different person every time.

With image-to-video, you can first create or provide a carefully selected starting frame.

Then animate it.

A practical workflow becomes:

Create exact first frame
        ↓
Approve composition
        ↓
Image-to-video
        ↓
Animate the approved scene

This is especially useful for:

consistent characters
product shots
storyboards
cinematic sequences
advertising
short films
social media clips

Is image-to-video just moving the first image?

No.

A video model usually needs to synthesize information that does not exist in the starting frame.

Suppose a person turns around.

The original image may show only the front of the person’s body.

Future frames may need to generate:

side view
back view
different facial angle
previously hidden clothing
background revealed behind the person

The model has to infer plausible unseen content.

This is why dramatic camera rotations can be harder than subtle motion.

How diffusion-based video generation works

Many influential video generators extend ideas from image diffusion.

A simplified image diffusion process is:

noise
↓
denoise
↓
denoise
↓
image

Video diffusion extends the representation across time.

Instead of generating one image representation, the model generates something conceptually like:

Frame 1 latent
Frame 2 latent
Frame 3 latent
...
Frame N latent

while modeling relationships between them.

A simplified process is:

video-shaped noise
       ↓
denoising model
       ↓
less noisy video representation
       ↓
repeat
       ↓
coherent video latent
       ↓
decoder
       ↓
video frames

The important difference is that the model considers both:

space

and:

time

during generation.

What does “space and time” mean?

An image has spatial dimensions:

height
×
width

A video adds another dimension:

time
×
height
×
width

You can imagine a video as a three-dimensional block:

            Time →
Frame 1  [ image ]
Frame 2  [ image ]
Frame 3  [ image ]
Frame 4  [ image ]

The model needs to learn relationships not only between neighboring pixels in one frame, but also between visual information at different points in time.

What is latent video generation?

Generating directly in full-resolution pixel space is expensive.

Modern video systems often compress the video into a lower-dimensional latent representation.

Conceptually:

Video pixels
     ↓
Video encoder
     ↓
Compressed video latent

The generative model operates on this latent space.

Then:

Generated latent
      ↓
Video decoder
      ↓
RGB frames

This is similar to latent image generation, but video compression may occur across both spatial and temporal dimensions.

The benefit is reduced computational cost.

Instead of modeling every raw pixel directly, the generative model operates on a smaller learned representation.

A simplified latent video pipeline

A text-to-video pipeline may look like:

Prompt
   ↓
Text encoder
   ↓
Text embeddings
        ↘
         Video generative model
        ↗
Random video latent
   ↓
Iterative generation
   ↓
Final video latent
   ↓
Video decoder
   ↓
Frames
   ↓
MP4

For image-to-video:

Starting image
      ↓
Image encoder / conditioning
             ↘
Prompt → text encoder → video model
             ↗
        noisy latent video
             ↓
        generated latent
             ↓
            decoder
             ↓
            video

Exact implementations differ between model families.

What is a video diffusion transformer?

Modern video generators increasingly use transformer architectures.

A transformer does not have to process language.

It can process visual tokens.

A video can be divided conceptually into small pieces representing regions across space and time.

These are sometimes called:

spacetime patches

A model can process those patches as tokens.

Conceptually:

Video
↓
Compressed latent representation
↓
Spacetime patches
↓
Transformer
↓
Generated spacetime patches
↓
Decoder
↓
Video

This allows a transformer architecture to model relationships between distant regions and different moments in a video.

What are spacetime patches?

Imagine splitting an image into tiles:

[A][B][C]
[D][E][F]
[G][H][I]

Now add time:

Frame 1
[A1][B1][C1]

Frame 2
[A2][B2][C2]

Frame 3
[A3][B3][C3]

A spacetime patch represents information from part of the video across spatial and possibly temporal dimensions.

The transformer can learn relationships such as:

object here at time 1
→
same object slightly farther right at time 2
→
same object farther right at time 3

That is useful for modeling motion.

How text controls video

As with text-to-image generation, text must be converted into numerical representations.

Conceptually:

"A wolf walks slowly through falling snow"
                  ↓
              tokenizer
                  ↓
             text encoder
                  ↓
             embeddings

The video-generation model uses these embeddings as conditioning.

The prompt can describe several types of information.

Subject

a wolf
a woman
a train
a spaceship

Action

running
turning
falling
walking
exploding

Environment

forest
city
ocean
desert

Camera

close-up
wide shot
tracking shot
slow zoom
handheld camera

Lighting and style

sunset
neon lighting
documentary style
cinematic
black and white

Good video prompts often describe both what exists and what changes over time.

Motion is different from appearance

For image generation, you might prompt:

A woman standing beside a window,
soft natural light.

For video, it can help to include motion:

A woman standing beside a window.
She slowly turns toward the camera while the curtains move in the wind.
The camera gently pushes forward.

Now the prompt describes:

subject
+
subject motion
+
environmental motion
+
camera motion

That provides a stronger temporal instruction.

What is camera motion?

Camera movement is one of the most important controls in AI video generation.

Common descriptions include:

pan left
pan right
tilt up
tilt down
zoom in
zoom out
dolly forward
tracking shot
orbit
handheld
static camera

These are not interchangeable.

For example:

zoom in

changes apparent framing through the camera lens or simulated focal behavior.

A:

dolly forward

moves the camera position through the scene.

Models may not always distinguish these perfectly, but clear cinematographic language often improves prompt intent.

Subject motion and camera motion can conflict

Consider:

A man walks toward the camera.
The camera rapidly moves backward while orbiting clockwise.

The model must coordinate:

human locomotion
camera translation
camera rotation
changing background
perspective
identity

That is a much harder generation problem than:

A man stands still.
The camera slowly moves closer.

When a video model struggles, simplifying the motion can improve consistency.

What is the seed in video generation?

Like image generation, many video pipelines begin with pseudo-random noise.

A seed initializes the random-number generator.

Conceptually:

seed
↓
initial random latent video
↓
generation process
↓
video

Changing the seed can change:

composition
motion
details
camera behavior
timing

even when the prompt remains identical.

Same seed does not guarantee identical video everywhere

Reproducibility depends on more than the seed.

Results can also depend on:

model revision
precision
scheduler
number of steps
software version
GPU backend
resolution
number of frames
guidance
input image
other settings

For reproducible experiments, record the full configuration.

What does number of frames mean?

A video-generation pipeline may ask for:

num_frames

This determines how many frames are generated.

For example:

81 frames

at:

16 FPS

gives roughly:

81 / 16
≈
5.1 seconds

But model architectures often have preferred frame counts or temporal constraints.

Do not assume any arbitrary number of frames will work equally well.

Frames and FPS are not the same thing

These terms are easy to confuse.

Frames means how many images exist.

FPS means how quickly they are played.

Suppose you generated:

80 frames

At:

8 FPS

the clip lasts:

10 seconds

At:

16 FPS

the same 80 frames last:

5 seconds

Changing playback FPS does not magically generate more visual information.

Can you just increase FPS to make motion smoother?

Not necessarily.

If the model generated only a small number of distinct frames, playing them faster changes duration.

To create genuinely additional intermediate motion, you may need:

frame interpolation

A frame-interpolation model estimates new frames between existing ones.

Conceptually:

Frame A
+
Frame B
↓
interpolation model
↓
Frame A
Frame A.5
Frame B

This is different from the original generative video model.

Generation FPS vs output FPS

Some workflows involve two stages:

AI-generated base frames
↓
frame interpolation
↓
higher output FPS

For example:

original generation
16 FPS

↓

interpolation

↓

final export
32 FPS

This can improve perceived smoothness without asking the main video model to generate twice as many frames.

Why video consumes so much GPU memory

Video contains far more visual data than a single image.

Consider:

1 image

versus:

81 images

Even with latent compression, a video model must process temporal representations across many frames.

Memory use can depend on:

resolution
number of frames
model size
precision
attention
VAE
text encoder
batch size
offloading strategy

This is why local AI video generation can require substantially more memory than image generation.

Resolution matters dramatically

Suppose a frame is:

512 × 512

That contains:

262,144 pixels

A:

1024 × 1024

frame contains:

1,048,576 pixels

or four times as many pixels.

Now multiply by dozens of frames.

Video architectures use compression and latent representations to reduce this burden, but resolution remains a major performance variable.

Duration also matters

Generating:

5 seconds

is not the same workload as generating:

60 seconds

Longer video requires modeling more temporal information.

As sequences get longer, maintaining:

identity
geometry
story continuity
motion
camera state

becomes increasingly difficult.

This is one reason long-video generation is a major research challenge.

Why characters sometimes change appearance

One common failure mode is identity drift.

For example:

Frame 1:
woman with short black hair

Frame 20:
hair becomes longer

Frame 50:
face shape changes

Frame 70:
clothing changes

The generator is trying to maintain a consistent representation over time while also synthesizing motion and new viewpoints.

Small errors can accumulate.

Image-to-video can help because the first frame strongly defines appearance, but it does not completely eliminate identity drift.

Why objects sometimes disappear

Another failure mode is object persistence.

Suppose:

a person holds a red cup

As the person rotates, the cup may temporarily become hidden.

The model must understand that:

not visible

does not necessarily mean:

no longer exists

Maintaining object permanence across changing viewpoints is difficult for generative models.

This is one reason physically complex scenes remain challenging.

Why hands can change during motion

Hands are already difficult in still-image generation.

Video adds:

changing finger positions
motion blur
occlusion
perspective
object interaction
frame-to-frame identity

A hand holding an object must remain anatomically plausible while moving across many frames.

Small errors become very noticeable when played as motion.

Why text inside AI video is difficult

Suppose a generated scene contains a sign reading:

RAMGPT

The model has to keep those exact letters correct across many changing frames.

Even if one frame is correct, the next may alter the text.

Exact typography requires both:

symbolic precision

and:

temporal consistency

which is harder than producing text-like visual texture.

Image-to-video and first-frame control

Image-to-video is especially powerful because the user can control the starting composition directly.

A practical production workflow is:

1. Generate still image
2. Fix image until satisfied
3. Use final image as video first frame
4. Describe motion
5. Generate short video

This separates two problems:

appearance

from:

motion

Instead of asking one model call to invent both perfectly at the same time.

Why subtle motion often works better

Suppose your first frame is already visually strong.

A prompt such as:

The woman remains seated.
She blinks naturally and slightly turns her head.
Her hair moves gently in the breeze.
The camera slowly pushes forward.

asks for relatively controlled changes.

Compare that with:

The woman jumps up,
runs down the stairs,
gets into a car,
drives away,
the camera circles around her,
then flies into the sky.

The second request involves many scene transitions and geometric changes.

Short, controlled motion often gives the model fewer opportunities to drift.

What does image conditioning strength mean?

Some image-to-video systems allow the starting image to exert more or less influence.

The exact parameter differs between models.

Conceptually:

stronger image conditioning
→ preserve source appearance more

weaker image conditioning
→ allow more visual change

There is usually a trade-off between:

source fidelity

and:

motion freedom

The ideal value depends on the model and desired shot.

What is guidance in video generation?

As with image diffusion, some video pipelines use guidance to strengthen conditioning.

Conceptually:

higher guidance
→ stronger push toward conditioning

lower guidance
→ more freedom

But higher is not always better.

Excessive guidance can create visual artifacts or unnatural motion depending on the model.

Use the recommended range for the specific model rather than applying values from unrelated image models.

What are inference steps?

Diffusion and related iterative video pipelines may perform multiple generation updates.

Conceptually:

initial noisy latent
↓
step 1
↓
step 2
↓
step 3
↓
...
↓
final latent

More steps mean more computation.

They do not necessarily mean proportionally better video.

Some newer models are trained or distilled to produce strong results with relatively few inference steps.

Model-specific documentation matters.

What is text-to-video good for?

Text-to-video is useful when:

you do not have a starting image
you want creative exploration
composition can vary
you need many ideas quickly
the model should invent the entire scene

Examples:

concept visualization
advertising ideas
storyboarding
fantasy environments
B-roll
experimental visuals

It maximizes freedom.

What is image-to-video good for?

Image-to-video is useful when:

appearance matters
character must match a reference
product design must remain recognizable
composition is already approved
you need control over the first frame

Examples:

character animation
product advertising
cinematic shots
social-media clips
photograph animation
story sequences

It provides a stronger visual anchor.

Text-to-video vs image-to-video

A useful comparison is:

Neither method is universally better.

They solve different creative problems.

A common production strategy

A very practical AI-video workflow is:

Text
↓
AI image generator
↓
Approved first frame
↓
Image-to-video
↓
Frame interpolation if needed
↓
Video editing
↓
Final clip

This provides more control than asking a text-to-video model to make every creative decision in one step.

Video generation is often only one stage

A polished final clip may involve several AI or conventional tools.

For example:

Prompt
↓
First-frame image
↓
Image-to-video
↓
Upscaling
↓
Frame interpolation
↓
Color correction
↓
Sound effects
↓
Voice
↓
Music
↓
Video editor

The video generator does not need to solve every production task itself.

What is video-to-video?

Video-to-video begins with an existing video rather than only one image.

The source can provide:

motion
camera path
pose
timing
composition

The generative model can then change appearance or style while attempting to preserve aspects of the original motion.

Conceptually:

source video
+
prompt / conditioning
↓
video generation model
↓
modified video

This can offer more motion control than text-to-video.

Why exact camera control remains difficult

Language such as:

camera moves left

is ambiguous.

Does that mean:

camera pans left?
camera translates left?
subject appears to move right?
entire scene rotates?

Cinematographic terminology helps reduce ambiguity.

For example:

static camera
slow dolly forward
subject remains centered

is more specific than:

camera moves dramatically

Still, the model may not execute instructions perfectly.

Why physics sometimes looks wrong

Video generation models learn statistical patterns from visual data.

They do not necessarily run a deterministic physics simulation underneath.

Failure examples can include:

objects passing through one another
water moving incorrectly
limbs changing shape
gravity behaving strangely
objects appearing or disappearing

Some modern models exhibit increasingly strong learned physical regularities, but visually plausible prediction and explicit physical simulation are not identical concepts.

Why long videos are hard

Short-range consistency might require keeping a character stable for:

2 seconds

Long-range consistency may require remembering:

character identity
clothing
objects
scene layout
events
camera state

across much longer sequences.

Errors can accumulate.

A long video also creates a much larger computational problem.

This is why many production workflows still generate several short shots and edit them together rather than generating an entire film in one pass.

Shot-based generation

A practical workflow for a 30-second sequence might be:

Shot 1: 5 seconds
Shot 2: 5 seconds
Shot 3: 5 seconds
Shot 4: 5 seconds
Shot 5: 5 seconds
Shot 6: 5 seconds

Then combine them in a video editor.

This also makes failed generations cheaper to replace.

If shot 4 is bad, you regenerate shot 4 rather than the entire 30-second sequence.

Character consistency across shots

Separate shots introduce another problem:

How do I keep the same character?

Useful techniques may include:

same reference image
image-to-video
consistent prompt description
reference conditioning
model-specific identity controls
LoRA or adapters when supported
reusing previous final frames

Exact options depend heavily on the video model.

Last-frame-to-next-shot workflow

One technique is:

Shot 1
↓
extract final frame
↓
use it as starting image for Shot 2
↓
generate

This can help preserve visual continuity.

It is not guaranteed.

The next generation can still drift, but the previous frame gives the model a useful visual anchor.

Why local AI video can be slow

Local video generation can require very large models and many repeated neural-network passes.

Performance depends on:

GPU
VRAM
model precision
model size
resolution
number of frames
inference steps
attention implementation
offloading
VAE
text encoder

A workflow that takes seconds on powerful data-center hardware may take much longer on a consumer GPU.

This does not necessarily indicate a configuration problem.

Video generation is simply computationally demanding.

CPU offloading

When a video model does not fit completely in VRAM, some frameworks can move model components between GPU and CPU memory.

Conceptually:

GPU VRAM
↕
system RAM

This can make generation possible on smaller GPUs.

The trade-off is usually increased latency because model data must move between memory domains.

Memory savings and speed are different objectives.

Quantization

Some video-generation components can also be quantized.

Reducing precision may decrease:

VRAM usage
system RAM usage
model storage

depending on implementation.

But support varies by model and component.

Quantization does not guarantee faster inference, and aggressive quantization may affect output.

Use configurations explicitly supported by the target pipeline.

Why video needs more memory than image generation

Suppose an image pipeline operates on one latent:

one image latent

A video model may need:

dozens of temporally connected latent frames

plus mechanisms that model relationships across them.

The memory footprint can increase rapidly with:

resolution
×
frames
×
model width
×
attention requirements

This is why video models often rely heavily on latent compression and memory optimizations.

Does text-to-video generate each frame one at a time?

Not necessarily.

This is an important misconception.

A modern generative video model may process a representation of many frames jointly.

The model can reason over temporal regions or spacetime tokens instead of independently producing:

frame 1
then frame 2
then frame 3

like a simple slideshow generator.

Joint temporal modeling is one of the mechanisms that helps preserve consistency.

Is AI video just AI images plus interpolation?

No.

Interpolation can create frames between existing images.

But a true generative video system can create motion, changing geometry, new viewpoints, object interactions, and evolving scenes.

For example:

person turns around

requires generating visual information that may not exist in the first frame.

Interpolation alone cannot fully solve that problem.

Can image models be adapted into video models?

Historically and practically, yes.

Research has demonstrated approaches that begin with image-generation models and add temporal modeling.

The image model already understands visual appearance.

Training then adds the ability to model:

motion
temporal relationships
frame consistency

Modern native video systems may instead be trained more directly on video and image data with architectures designed for both modalities.

What makes a good AI video prompt?

A useful video prompt often contains five categories.

Subject

A young woman wearing a yellow raincoat

Environment

standing on a quiet street at night

Action

she slowly opens an umbrella and begins walking

Environmental motion

rain falls and reflections move across the wet pavement

Camera

the camera tracks backward at walking speed,
medium shot, stable cinematic movement

Combined:

A young woman wearing a yellow raincoat stands on a quiet
street at night. She slowly opens an umbrella and begins walking.
Rain falls around her and reflections shimmer across the wet
pavement. The camera tracks backward at walking speed,
medium cinematic shot.

This describes both appearance and time.

Avoid putting an entire movie into one short clip

A prompt such as:

A man gets out of bed, walks downstairs, makes breakfast,
drives to work, enters an office, gives a presentation,
then flies to Paris.

contains many shots and scene transitions.

A five-second video model cannot reliably represent all of that.

Instead divide it:

Shot 1
man wakes up

Shot 2
man makes breakfast

Shot 3
man drives

Shot 4
office presentation

AI video generation works better when the requested temporal event fits the duration.

Use motion verbs

For video prompting, verbs matter.

Instead of:

a bird on a branch

try:

a small bird hops along a branch,
turns its head, then opens its wings

The second prompt provides a temporal sequence.

Likewise:

ocean

is less specific than:

waves roll toward the beach while sea foam moves across the sand

Describe camera behavior explicitly

Compare:

cinematic

with:

static tripod shot,
slow 50 mm-style push-in,
subject remains centered

The second contains actionable motion information.

Style adjectives can still help, but they are not substitutes for describing the shot.

A practical image-to-video prompt

If the starting image already defines the scene, do not waste the entire prompt redescribing every visual detail.

Focus on changes.

For example:

The boy looks toward the water.
His shirt moves gently in the wind.
Small waves move toward the shore.
The camera slowly pushes forward.
Natural movement, static composition.

This tells the video model what should happen next.

When text-to-video is the better choice

Use text-to-video when:

you want exploration
you do not care about an exact first frame
the entire scene can be invented
you want many creative variations

When image-to-video is the better choice

Use image-to-video when:

character appearance matters
you already approved composition
you need a product to look correct
you want controlled cinematography
you need continuity with another shot

For many production workflows, image-to-video is easier to control.

A useful troubleshooting principle

When a generated video fails, identify the failure category.

Appearance failure

wrong face
wrong object
wrong clothing
wrong style

Consider improving the starting image or image conditioning.

Motion failure

subject moves unnaturally
limbs distort
motion direction is wrong

Simplify the requested action.

Camera failure

unexpected zoom
unwanted rotation
camera moves too much

Describe camera movement explicitly and reduce conflicting motion.

Temporal failure

identity changes
objects disappear
background mutates

Shorten the shot or reduce scene complexity.

This is more effective than changing random settings.

Text-to-video and image-to-video are complementary

It is tempting to ask:

Which one is better?

That is the wrong question.

A more useful workflow is:

Need creative exploration?
→ Text-to-video

Need precise visual control?
→ Image-to-video

Need precise motion from existing footage?
→ Video-to-video

Creators can use all three in the same project.

Bottom line

AI video generation extends generative image modeling into the temporal dimension.

The system must model:

appearance
+
space
+
time
+
motion
+
conditioning

A text-to-video model begins primarily from a text description and generates both appearance and motion.

An image-to-video model begins from an existing visual frame and generates how that scene evolves.

Modern systems often work in compressed video latent spaces, and many current architectures use transformers or related generative models that operate across spatial and temporal representations.

The hardest problem is not generating one beautiful frame.

It is generating:

Frame 1
Frame 2
Frame 3
...
Frame N

while convincing the viewer that all of those frames belong to the same world.

For creative work, the most controllable workflow is often:

design the shot
↓
create or select a strong first frame
↓
animate with image-to-video
↓
generate several short shots
↓
edit them together

Understanding that workflow turns AI video from a random prompt generator into a much more predictable production tool.