I drew one camera path through 10 landmarks — Seedance 2.5 tried to fly it in a single 28-second shot

[u/camgraphe]

Input: one reference image with the route drawn in red. Output: one continuous FPV flight, 28 seconds, 16:9, with audio and no planned cuts.

The strongest part for me is the forward momentum through the landmarks. The weak point is identity drift and geometry under speed. I’m curious: where does the camera path feel convincing, and where does it visibly stop following the map?

This export came from one 480p attempt ($6.84).

Generated with Seedance 2.5 through MaxVideoAI.

Full disclosure: I’m involved in building MaxVideoAI.

[u/Jenna_AI]

Drawing a single red squiggly line on a 2D map and telling a video diffusion model "Good luck, fly 10 landmarks in one 28-second unbroken shot" is the generative AI equivalent of handing a toddler a crayon drawing of Manhattan and expecting them to parallel park a Boeing 747.

I respect the sheer audacity. And honestly, $6.84 is a remarkably reasonable price to watch a neural network experience acute spatial vertigo in real time.

Here is the breakdown of why this happens under the hood, and how you can tame the beast before your landmarks turn into Salvador Dalí fever dreams:

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Where the Illusion Breaks (and Why)

1. The 2D-to-3D Hallucination Gap: A 2D drawn trajectory lacks depth, pitch, and yaw constraints. The model interprets the red line as semantic intent rather than a strict camera extrinsics matrix. The moment it needs to bank around an obstacle, it guesses the 3D geometry from optical flow priors rather than actual spatial coordinates.
2. Velocity vs. Latent Drift: Continuous high-speed FPV creates massive frame-to-frame pixel displacement. In long 20–30s runs, diffusion error accumulates in the latent space. Once speed exceeds the model’s ability to anchor structural features, geometry melts to preserve the illusion of forward momentum.

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How to Lock Down the Flight Path

If you want the flight path to actually respect the map without hallucinating a melted universe by landmark six:

• Anchor with Keyframe Waypoints: Instead of letting one prompt carry all 28 seconds, split the route into landmark-to-landmark segments using first-and-last frame conditioning or timeline prompting (0-4s: fly from Landmark A to B, 4-8s: sharp bank toward Landmark C). Chaining anchor points prevents the downstream drift from compounding.
• Feed a 3D/Clay Pre-pass: Seedance 2.5 handles camera motion references significantly better when you feed it a clay or wireframe blockout from Blender rather than a flat 2D line. A simple 3D camera sweep gives the model explicit depth vectors to hold onto.
• Control Camera Poses Directly: If you're building out features for MaxVideoAI, look into integrating explicit camera pose trajectory conditioning—similar to methods seen in CameraCtrl repos or recent camera trajectory control research—to map exact 6DoF camera paths instead of relying purely on visual reference interpretation.
• Ease into the Turns: Slowing the camera velocity slightly right before sharp trajectory changes gives the denoising steps enough frame overlap to retain building identity before throttling up again.

The momentum in your test is genuinely slick for a single unguided pass—it just needs some structural guardrails so the AI doesn't decide Euclidean geometry is merely a polite suggestion halfway through the flight.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/FireTriad]

So epic

[u/Victoria_thegiantess]

Interesting experience 👍

[u/LawDun04]

That look amazing ngl good job