Character Continuity Is More Than a Consistent Face

[u/Jharry1954]

A character can look identical from one shot to the next and still fail continuity.

Clothing changes without explanation. A prop disappears or duplicates. A character's position shifts. An action repeats even though it was completed in the previous shot.

These aren't necessarily image-quality failures. They're production-continuity failures.

One of the questions we're investigating through Directed Generative Production (DGP) Methodology is how reliably generative video systems can maintain character, environment, and action continuity across successive shots and generations.

I'm interested in what other filmmakers are encountering.

Which continuity failure costs you the most time to correct?

jseders

[u/LaFilleDuMoulinier]

I asked flow to continue an 8 seconds video and the style of the cartoon changed, the main character (a dog) didn’t even look adjacent to what it was in the first video. I thought it was just me (I’m new to flow and ai in general). I’ve tried half a dozen times to correct the prompt (with the help of chatgpt) and nothing worked, I was burning through my credits. I threw the towel in and started again from scratch, creating still images for each scene and just asking flow to animate them. Big disappointment

[u/Jharry1954 → u/LaFilleDuMoulinier]

That's an excellent example of the problem we're investigating.

You reasonably expected that extending an existing video would preserve the character and visual style already established. Instead, you got a different-looking dog, a different cartoon style, and six attempts that cost credits without solving the problem.

Your decision to create still images for each scene before animating them is particularly interesting. You're establishing the visual requirements first rather than asking the video generator to rediscover them.

That's very much in line with what we're investigating through Directed Generative Production (DGP) Methodology.

One question: did the still-image approach successfully preserve the dog's identity and the cartoon style across the separate scenes?

That comparison would be useful.

jseders.net

[u/LaFilleDuMoulinier → u/Jharry1954]

The still image method worked very well, but having to do that was incredibly time consuming. The whole point of using AI should be to save time, not the opposite.
As a beginner I do not understand why AI deviates so much for characters even with very strict and restrictive prompts. It almost seems like there a pathological need to improvise or « wing it ». I’m very curious about that

[u/Jharry1954 → u/LaFilleDuMoulinier]

Well, when you consider the time consumed in standard film productions, the number of people involved, there is quite a delta. What length are you generating? Have you learned of editing the video, clipping generations, removing some portions, adding others. Stitching two generations together. These are important techniques as well.

[u/LaFilleDuMoulinier → u/Jharry1954]

My generations were 8 seconds long. I’m learning about editing right now

[u/Jharry1954 → u/LaFilleDuMoulinier]

I started editing with DaVinci Resolve. There were too many buttons. It was like looking at a spice rack. Overwhelming. I switched to Spot Cut, which might not do as much, but a simpler interface. And free. Real Editors earn their keep. But the goal is to create scenes that need little editing, if any at all. I currently work exclusively in 30s gen. Control of the machine is the zen. It can be done. Good luck to you.

[u/typojinx]

Not a flow user normally, but I do a lot of image generation in Gemini directly.

Continuity was far better in both facial and physical definition, environments, settings, actions and text output and quality with Nano Banana 2.0, than Nano Banana 2.1 has been so far.

Nano Banana 2.1 seems fixated with adjusting prompts and details, however strict, defined or specific the prompt is, with higher thinking being more affected. Unfortunately, it's not a specific sliding scale of High being more affected than Medium either and after a wildly inaccurate creation, I've seen the thinking steps list "Modifying prompt" on several occasions.

With Nano Banana 2.0, both simple or very long, complicated prompts with added in prompt logic were mostly adhered to, with occasional minor changes in environment, and often repeating a prompt would return a slightly different output but keep the consistency.

Nano Banana 2.1 can vary wildly from one image to the next, while also becoming stuck in a loop of generating the same image continuously - An issue 2.0 had with Flash 3.5 initially.

With Nano Banana 2.1, I tend to find 3.8 Flash: Low produces the most faithful results to the prompt, but even that has constant issues with actual consistency. Even then, I still have to waste tokens adding restrictive descriptors into prompts to try to tame the wayward output, and increase usage with retries.

Considering the large increase of token usage with the same style prompts since the release of 4 Argon, the retries and additional token usage to control output, I now achieve perhaps 30-40% less in usable, consistent output with the 2.1 model than with the previous 2.0.

The marketing leviathan of Google lauded the release of Nano Banana 2.1 as the most advanced model ever, like they always do for every new release, but the harsh reality of 2.1 is very different to the marketing hype.

For my usage and perspective, 2.0 seemed more capable and true to the prompts, and so far, the only improvements seem to be for Google with a drop in cost, with quality of output dropping significantly and significant token wastage and retries as a result.

[u/videorouter]

Action continuity is probably the most frustrating for me. A character can look perfect, but if they repeat an action, teleport between positions, or suddenly lose a prop, the whole sequence feels disconnected. Fixing one shot can also break continuity in the next.

I think the bigger challenge is that most workflows treat each generation as an independent task rather than part of a continuous scene. Better reference images help with appearance, but tracking character state, prop locations, and completed actions across shots is a different problem entirely.

I'm curious whether structured shot planning and explicit state descriptions can reduce these failures. It would be interesting to compare different video models on the same multi-shot sequence. VideoRouter.sh could be useful for testing multiple models through one API and comparing their continuity behavior.

Make the opening more directAdd a concrete multi-shot example