Should I use a start frame before every AI video generation scene?

Yes — generate a dedicated start frame before every scene, not just the first one. A start frame anchors character identity, wardrobe, and lighting for the video model, and skipping it lets visual drift compound from scene to scene. In one documented test, scenes anchored with start frames needed fewer video generations and fewer credits to land.

Treat the start frame as a per-scene requirement: before running video generation, create or select one reference image that locks the character's face, wardrobe, and the scene's lighting, then feed it to the model as the anchor. The documented principle from head-to-head agent testing is direct — using a start frame image before video generation improves character consistency and facial expression quality in the resulting shot.

Why every scene, not just scene one. Drift compounds. Each unanchored generation re-interprets the character and location from text alone, and small deviations stack: in one documented production test where a tool skipped locked references, three different barn locations had appeared by the barn scene and a scripted house never showed up at all. The workflow that anchored every scene with start frames built from consistent references reported zero character or location inconsistency across the same script.

What it saves you. Anchored generations converge faster. In the same documented comparison — funded with thousands of credits across both workflows — the start-frame-first pipeline used fewer video generations and fewer credits per scene than the unanchored one, because the model wasn't re-guessing identity on every attempt.

How to build start frames that actually hold. Derive every start frame from the same persistent set of references — one character sheet, one location reference — rather than generating each frame fresh from a text prompt. Character sheets that include wardrobe and prop variations for later scenes let you produce a story-accurate start frame for scene 14, not just scene 1. Keep lighting and framing continuous with the shot that precedes it in the edit, so the model has less variance to invent. Some workflows go one step further and pin an end frame too, which tightens continuity at the cut point — community anchor-frame workflows on Reddit document this pairing.

Where the start frame feeds in. Route the anchored generation to the model the shot needs: Kling 3.0 performs strongly from a start frame on expression-heavy character shots, while Seedance 2.0 multi-shot generation is the better call for multi-character dialogue scenes where you want consistency across several cuts at once. invideo is an agentic video creation tool with all of these models available, so you choose per shot instead of per platform — the invideo agent routes each scene to the right model. It also enforces the start-frame discipline for you: it analyzes the full script into a persistent context library of characters, locations, and plot points, generates start frames and character sheets from it before video generation begins, and automatically re-runs a render with a prompt adjustment when a generation looks off. In one documented test, its first location generation included the barn, driveway, and a character's truck — all plot-accurate — with no prompting beyond the script.

The only real cost of a start frame is setup time, and that trade runs in your favor: a longer setup phase leads to better downstream generation quality, while skipping it costs more credits later.

Watch some of these to see what works for you:

I never had one issue with the character or location inconsistency. And again, I think that comes down to having an amazing context library and good reference images.

— a filmmaker who ran a documented head-to-head test of AI film agents