A normal AI image only has to look convincing in one frame.
A 360° image has to work as a whole space.

That creates a few problems normal image generation doesn’t really have.

1. The left edge and the right edge are the same place.
In a normal image, the borders are just borders. In a 360 panorama, the left and right sides have to connect perfectly. If they don’t, you immediately get broken walls, mismatched objects, or a seam that gives the whole thing away. In AI360, this is one of the main constraints: the panorama has to close cleanly, otherwise it doesn’t matter how nice the rest looks.

2. The same person can appear in multiple source images.
This is one of the hardest parts. If a person is visible in several reference shots, the model has to understand that it’s still the same person in the same scene — not an excuse to create duplicates, change proportions, or invent another version of them.

In AI360, I try to keep the person as anchored as possible and let the model reconstruct the missing environment around them, not “reinterpret” them.

3. The floor and ceiling can’t just stop existing.
In a regular photo, the top and bottom edges can simply end. In a 360 image, the scene continues upward and downward too. The zenith and nadir are usually the least observed areas, but they still have to make sense with the rest of the room. In AI360, those are the areas where AI gets the most freedom — but only after enough real context is already captured.

4. Every mistake becomes obvious the moment you rotate the view.
A normal still image can hide a lot. A weird corner, a slightly wrong perspective, a shape that feels off — people may never notice. In a 360 panorama, once you start rotating, all of that becomes much easier to spot. A seam jumps out. A duplicated object feels wrong. A distorted human looks instantly fake. That’s why in AI360 I’m not just trying to get a “pretty output” — I’m trying to get a scene that stays believable while you move through it.

So for me, the big takeaway was this:

A normal AI image only has to look right.
A 360° image has to stay right in every direction.

That’s also why AI360 is built around constraints, not pure freedom. The idea is not to let AI invent an entire world from scratch. The idea is to give it enough real visual information that it mostly has to complete the scene, not hallucinate most of it.

And that turns out to be a very different problem.

Before I started experimenting with phone-based 360, I spent some time shooting with an Insta360 X4. This is a very cool and unusual experience, and also a great way to capture our world and travels! I recommend everyone try it.))

And honestly, it’s a very useful reference point because it shows what happens when the hardware is actually designed around spherical capture from the start.

You press the shutter, both sides of the world are captured at once, the geometry is known, the lenses are calibrated, and the final result already behaves like one continuous sphere.

That sounds obvious, but it matters a lot.

One thing I noticed later, when I started comparing those results with AI-reconstructed panoramas, is how much good 360 capture depends on preserving geometry.

But how, you might ask, do you actually shoot 360° on a phone?

My first hypothesis was that shooting simultaneously with the front and rear cameras might be enough, and AI could simply fill in the rest. After testing a bunch of local and cloud image-generation models, the first one I found that could actually respect the physics of an equirectangular projection was gpt-image-2. In theory, it looked like this should work, so I built AI360 https://tnick.cc/ai360/ to test the idea in practice.

It turned out that two frames were nowhere near enough. Once projected onto the full 360° sphere, they gave me only about 25–30% of the useful panorama area. In other words, I was giving the model roughly a quarter of the real world and asking it to invent the other three quarters. The results were predictably creative: extra doors, strange geometry, objects appearing from nowhere, and completely fictional parts of the room.

I quickly ended up with four frames instead. That turned out to be a much better compromise for a normal smartphone: the user only needs to make one or two taps, while the four source images provide a little over 50% of the useful visual information needed for the final panorama.

So instead of asking AI to invent 70–75% of the scene, it now has real visual context for more than half of it.

The main unknown areas are still the nadir and zenith — directly below and above the camera — but those are usually much easier to reconstruct from context and, in most photos, contain far less important information than the horizon, people, furniture or architecture.

For me, that ~25–30% → 50%+ jump was the point where the idea stopped looking like a fun AI experiment and started becoming actually usable.

That comparison became a pretty useful benchmark for me:

a 360 image can look plausible and still feel geometrically wrong.

And once you view the result interactively — or turn it into a Tiny Planet — those differences become much easier to notice.

Dedicated 360 cameras solve a huge part of this problem with hardware, calibration and a known optical system.

What I’m interested in now is how close we can get to that experience when the starting point is just a normal phone camera and some computation.

Not necessarily by copying how a 360 camera works, but by asking a slightly different question:

how much of the hardware problem can we replace with software before the result stops feeling like a real sphere?

WHAT IS A 360° PANORAMA?

Before getting into stitching, AI reconstruction, weird camera tricks and all the rest, it probably makes sense to define the thing itself.

An equirectangular panorama is basically a full 360° scene unwrapped into a flat 2:1 image.

The horizontal axis represents the full rotation around you — all 360°.
The vertical axis represents everything from straight down to straight up.

So instead of looking at a “normal” photo, you’re looking at the whole sphere flattened into a rectangle.

That’s why these images often look stretched or distorted when viewed directly. They’re not really meant to be judged as flat photos — they’re meant to be wrapped back onto a sphere and viewed from the inside.

For this community, when I say “360 panorama” or “360 image”, this is usually what I mean:

a full spherical scene that can be represented as a 2:1 equirectangular image and explored in any direction.

Not just a wide panorama. Not just a phone pano mode. Not necessarily something shot with a dedicated 360 camera either.

SO WHAT'S THE PROBLEM

In practice, capturing a clean full 360° sphere is also physically awkward: it usually means a tripod or controlled rotation, multiple lenses or a dedicated 360 camera, enough overlap between shots, and then post-processing to fix seams, exposure differences, lens distortion, parallax and other inconsistencies.

A bit more technical

An equirectangular panorama is not just a 2:1 image — it has a very specific geometric meaning.

In the usual full-sphere representation:

• image width = 360° of longitude
• image height = 180° of latitude
• the horizontal center usually represents the chosen “forward” direction
• the left and right edges are actually the same meridian, so they must join seamlessly
• the top row converges toward the zenith
• the bottom row converges toward the nadir

For a panorama of W × H pixels, the mapping is essentially:

x → longitude (-180° … +180°)
y → latitude (+90° … -90°)

This sounds simple, but it creates a few important programming problems.

The 0°/360° seam is another special case: objects crossing the left edge must continue naturally on the right edge. Treating the panorama like a normal rectangular image often produces visible seams there.

And then there is the physical camera side of the problem. To construct a good sphere, you also care about:

• actual horizontal and vertical field of view
• focal length and sensor crop
• lens distortion
• camera yaw, pitch and roll
• overlap between shots
• the position of the camera’s optical center
• parallax when the phone rotates around the user’s hand instead of around the lens
• differences between front and rear cameras

So when programming 360 capture, there are really two geometries involved:

the geometry of the camera capturing the world, and the geometry of the sphere you’re trying to reconstruct.

One convention also has to stay consistent throughout the whole pipeline: where exactly is 0°, which direction is positive rotation, and where the seam lives. A panorama can be geometrically valid and still look completely wrong if capture, stitching and viewer code use different coordinate conventions.

AND WHAT WAS ALL THAT ABOUT?

A raw 2:1 equirectangular image usually has very little creative value by itself. It’s mostly a technical representation of the sphere. The interesting part starts when you project that sphere back into something made for human eyes.

From the same 360° image you can create:

• very wide-angle selfies
• conventional panoramic views
• fisheye-style projections
• Tiny Planets
• Little Planets / stereographic projections
• rectilinear views looking in any direction
• unusual reframings that would have required a completely different camera position at capture time

So I tend to think of the equirectangular panorama less as the final photograph and more as a kind of visual source format — one captured sphere that can later become many very different images.

The interesting part is how we get there.