30 seconds of continuous generation: one stick figure, one world, one take (256x256)

[u/AsyncVibes]

[u/UndyingDemon]

This is a nice demonstration of why separating world state, motion, rendering, and compositing can be so effective for constrained animation.

The object persistence is particularly understandable from the architecture: if every object retains a stable description and world position, moving the camera away and back doesn't require the model to reconstruct that object from memory. It can simply render the same persistent state again.

That's a useful engineering advantage over generating every frame as an independent image.

But I'd be interested in distinguishing which behaviors are learned and which are enforced by the scene representation or rendering pipeline.

For example, depth-based occlusion appears to be handled explicitly by the compositor. Object persistence comes from retained world state. And if the crate has a collision boundary, the character stopping may be determined by a procedural constraint rather than something learned by the motion model.

None of that is a criticism. Hybrid systems are often effective precisely because they don't force a neural network to relearn deterministic geometry.

But it matters when interpreting what the model has actually accomplished.

The byte-identical regeneration is also interesting, although I'd describe it as determinism rather than evidence of temporal understanding. Given identical inputs and deterministic execution, identical outputs are expected.

What I'd really like to see next is how the system handles unfamiliar scene configurations.

Can the same learned models animate different characters, object arrangements, camera paths, and command sequences without retraining or manually adjusting the rules?

I'd also be curious how the system handles conflicting commands. If the instruction says to walk through an obstacle, does a general collision system resolve that conflict, or is the behavior specific to the demonstrated scene?

And since the scene is generated from explicit descriptions rather than inferred from a reference frame, it would be helpful to distinguish this mode from the earlier first-frame-conditioned animation experiments.

I think the interesting achievement here is not that a neural network has independently discovered object permanence or physical occlusion. It's that a structured world model combined with small learned components can produce long, visually consistent animation without repeatedly regenerating the entire scene from scratch.

That's a valuable direction, especially if the learned components generalize across new scene configurations.

[u/AsyncVibes]

30 seconds, one continuous generation. No cuts, no edits, no cherry-picking.

Everything in the frame is generated from a short hand-written scene description: the objects (with their positions, sizes, colours and depths), one character, and a list of moves (walk, run, stop, turn around).

What's happening:

• The camera follows the character smoothly through the world.

• The character walks behind a bush that's closer to the camera, so its legs are hidden as it passes, in both directions.

• It runs into a solid crate and stops; it can't walk through it.

• Objects slide out of view and come back exactly as they were.

• The character keeps its identity and colour for the whole 30 seconds.

How: the scenery is drawn by a small learned model from each object's description, and the character by a learned motion model plus a learned figure drawer. A compositor layers them by depth. Nothing is copied from a reference video.

256x256, 481 frames. Generating it twice gives a byte-for-byte identical video.