Video
Consistent Character AI Video on Martini
Lock the protagonist across every cut. Where the parent feature ai-character-consistency keeps identity stable across image and video, this page is the video-specific delivery — the same person, same face, same wardrobe across every shot, every camera move, every scene transition. Reference-driven video models, anchored on Martini's canvas, hold the character through motion.
What this feature solves
Episodic and serialized AI content lives or dies on whether the protagonist looks like the same person across cuts. Single-prompt video tools regenerate the face every time, so cut one might land a sharp likeness while cut three drifts to a younger, narrower-jawed stranger. For an AI influencer running a weekly series, a brand spokesperson appearing across a campaign, or a recurring character in a multi-episode show, that drift is the difference between content the audience trusts and content they swipe away from.
The break compounds when the shot list is long. A thirty-second product spot with eight cuts, a multi-episode series with ninety beats, an ad campaign with forty placements — each individual generation is independent and the cumulative drift is enormous. Tab-based video tools force you to re-paste the reference into every session, hope the engine respects it, and curate by hand. There is no way to confirm continuity at scale, and identity collapses by the time the producer is reviewing the cut.
The other side is cross-shot continuity. Even when one shot lands the character beautifully, the second shot may land a different wardrobe, a different scene lighting, a different age read. Reference-driven video models help, but only if the reference is anchored once and reused across every shot — and only if you can chain image refinement through to video so the exact still that holds the character is the still that drives the motion.
Why Martini is different
Martini's canvas treats the character reference as a node that wires into every video shot in parallel. Drop the canonical portrait once, then connect it to as many video nodes as the cut requires — each driving Vidu, Kling O3, Kling 3, or Kling Avatar with the same anchor. The video models that hold characters best on the market are the ones that listen to a reference, and Martini puts that reference inside the chain rather than asking you to re-upload it per generation. Eight cuts of the same person become a fan-out off one node, not eight independent guesses.
Image-to-video chaining is the second weapon. Generate a hyper-clean character still on Nano Banana 2 or Flux Kontext — locked face, locked wardrobe, exact pose — then feed that still into Vidu or Kling 3 video nodes. The video model anchors to a refined still rather than a raw reference, and the identity holds far better through motion than if the original portrait went straight into video. The chain image → image → video is the production pattern that Martini's canvas makes natural, and it is the difference between a one-take spokesperson and a series-grade recurring character.
Sequence building locks the cut. Once each shot has its winning take, the sequence builder packages them in cut order with consistent frame rate and codec. NLE export drops the bundle into Premiere Pro, DaVinci Resolve, or Final Cut Pro at clean specs. The character travels intact from canvas reference to image refinement to video shot to packaged sequence to editor timeline — a single chain of custody for identity, which is what serialized AI content actually requires.
Common use cases
Multi-cut ad spot starring an AI spokesperson
Run eight cuts of the same spokesperson across product, lifestyle, and talking-head shots with the face locked through the entire spot.
Episodic AI series with a recurring protagonist
Lock one lead across every episode, every location, every wardrobe — for a serialized show or branded narrative.
Weekly AI influencer video drops
Reuse the same character canvas template every week and only swap the script, location, and outfit prompt.
Brand spokesperson in product cuts and explainers
Keep the spokesperson identical from the hero shot to the explainer to the talking-head close-up so the campaign reads as one piece of content.
Storyboard-to-cut continuity for a short film
Animate every storyboard frame with the same protagonist, then sequence the cuts so the rough cut looks like a single continuous performance.
Pre-vis for a multi-talent live-action shoot
Lock stand-in characters before booking talent so the team can review wardrobe, blocking, and continuity in moving pre-vis.
Recommended model stack
vidu
videoReference-driven video that holds the same subject across multiple clips reliably.
kling-o3
videoCharacter-aware motion for spokesperson, dialogue, and talking-head cuts.
kling-3
videoCinematic camera language with strong character anchoring across motion.
kling-avatar
videoAvatar-grade lip-sync and dialogue with identity preserved across delivery.
nano-banana-2
imageRefines a canonical character still that anchors the downstream video chain.
flux-kontext
imageOutfit and scene changes on the canonical character before it enters video.
How the workflow works in Martini
- 1
1. Lock the canonical character still
Generate or upload one strong portrait — clean lighting, sharp likeness, neutral pose. Refine it on Nano Banana 2 if needed. This image is the source of truth for every video shot.
- 2
2. Wire the still into a Flux Kontext node for outfit and scene variants
Each shot in the cut probably has different wardrobe and different surroundings. Flux Kontext applies those changes while preserving the face — the output of each Kontext node becomes the per-shot character anchor.
- 3
3. Chain each shot-specific still into a video node
Wire the per-shot still into Vidu, Kling 3, Kling O3, or Kling Avatar. The video model anchors to the refined still and keeps the identity through motion.
- 4
4. Fan out across video models for hero shots
For a hero close-up or a dialogue cut, run two or three video models in parallel and pick the take that best preserves the face under motion.
- 5
5. Add lip-sync and audio if dialogue is required
For talking-head and spokesperson cuts, chain the chosen video clip into a lip-sync node with ElevenLabs voice. The character speaks in your scripted voice without losing identity.
- 6
6. Sequence and export the cut
Drop every winning take into the sequence builder in cut order, then NLE export to Premiere Pro, DaVinci Resolve, or Final Cut Pro at the frame rate and codec your editor expects.
Example workflow
A DTC fashion brand is launching a recurring AI spokesperson named Lina for a 12-week vertical-video series. Week one, the team locks Lina's canonical portrait on Nano Banana 2, then runs Flux Kontext to generate twelve outfit variants — one per week. Each variant feeds into a Vidu video node with a 9:16 brief: "medium close-up, spokesperson direct address, soft natural light." The hero week-one cut runs in parallel across Vidu, Kling 3, and Kling O3; Vidu wins. The chosen clip chains into a lip-sync node with ElevenLabs voice for Lina's scripted line. Sequence builder packages a 20-second drop and NLE export sends it to Premiere as ProRes 24p. The same canvas becomes the template for week two — swap the outfit, swap the script, re-render. Twelve weeks of consistent Lina, locked across every cut.
Tips and common mistakes
Tips
- Refine the canonical still on Nano Banana 2 before video. A sharper anchor produces a sharper character through motion.
- Use Flux Kontext to vary outfit and scene without re-generating the face. The face stays locked, the surroundings do not.
- Vidu and Kling O3 are the strongest video engines for character continuity in this lineup. Lead with them for hero cuts.
- For dialogue cuts, chain video → lip-sync rather than asking the video model to handle lip movement directly. The two-step chain is more controllable.
- Save the character canvas as a template the moment week one lands. The series template becomes the production unit, not the per-shot generation.
Common mistakes
- Feeding the raw reference portrait into video models. The image → image → video chain holds character far better than image → video.
- Mixing two different reference portraits in the same chain. The model averages them and the identity collapses.
- Asking one video model to handle both motion and dialogue lip-sync end-to-end. The lip-sync feature, run separately, gives cleaner results.
- Letting the lighting drift between shots. The face is locked but the lighting changes the perceived identity — keep the prompt consistent on light language.
- Skipping the canvas template. Character series content scales on template reuse, not per-shot improvisation.
Related how-to guides
Related models and tools
Tool
AI Lip Sync
Lip-sync tools on Martini for syncing voice and dialogue to portraits and video.
Tool
AI Video Frame Extraction
Extract frames from video for reference and image-to-video workflows.
Provider
Kling
Kling 3, O3, and Avatar video model workflows on Martini.
Provider
Vidu
Vidu's reference-driven video and character consistency workflows on Martini.
Provider
Google's Veo video, Imagen image, and Nano Banana model workflows on Martini.
Provider
ByteDance
ByteDance's Seedance video and Seedream image model families on Martini.
Related features
AI Character Consistency Across Images and Video
Keep a subject consistent across image and video generations on Martini using reference workflows.
AI Character Reference — Reference-Image Workflows on Martini
Use reference images to guide AI model outputs on Martini's canvas.
AI Video Reference Images — Preserve Subject and Style
Lock subject, character, and style across every video generation on Martini's canvas — Vidu, Kling O3, Seedance 2, Nano Banana 2 reference workflows.
AI Influencer Video Generator — Repeatable Character Pipeline
Design, generate, and scale AI influencer videos on Martini — character library, voice cloning, lip-synced video, all on one canvas.
AI Image to Video — Animate Stills Into Production-Ready Shots
Turn still images into production-ready video shots on Martini's canvas — multi-model, reference-aware, NLE-export ready.
Multi-Shot AI Video — Build Connected Scenes, Not Isolated Clips
Plan, generate, and sequence multi-shot AI video on Martini — keep characters, style, and motion consistent across shots.
AI Product Video Generator — From Product Image to Ad Video
Create product ads and demos from product images on Martini's canvas — chain product photo to multi-shot video across Seedance, Runway Gen-4, and GPT Image.
AI Ad Creative Generator — Multi-Format Ad Visuals and Video
Generate ad visuals and videos across Ideogram, Flux, Seedance, and Runway on Martini — every aspect ratio, every variant, one canvas.
AI Avatar Video Generator — Talking Avatars from Image and Audio
Create talking avatar videos from image and audio on Martini's canvas — Kling Avatar, OmniHuman, ElevenLabs, locked identity across every clip.
AI Talking Head Video — Spokesperson, Course, and Narration
Produce spokesperson, course, and narration videos on Martini's canvas — Kling Avatar, OmniHuman, ElevenLabs, Fish Audio, locked identity end to end.
Video to Video AI — Restyle, Edit, Transform Source Footage
Restyle, transform, and edit source video on Martini's canvas — Runway Aleph, Kling O3, Wan chained into multi-shot pipelines.
AI Video Generator — Multi-Model AI Video Production on Martini
Multi-model AI video generation with text, image, reference, and editing workflows on Martini's canvas.
Text to Video AI — Generate Video From Prompts on Martini
Generate video from prompts and chain outputs into scenes on Martini's multi-model canvas.
AI Explainer Video — Educational and B2B Demo Videos
Generate explainer videos, B2B demos, and educational content on Martini's canvas.
Related docs
Related reading
Comparisons
Frequently asked questions
How is this different from ai-character-consistency?
ai-character-consistency is the cross-modal hub — it covers identity preservation across image and video. This page narrows to the video-specific delivery: the same person across every cut, every camera move, every scene transition. Use the parent for the overall outcome; come here for the multi-shot video workflow specifically.
Which model holds character best in video?
Vidu and Kling O3 lead this lineup for character continuity. Kling 3 holds well on hero shots with strong camera moves. Kling Avatar is best when the cut needs lip-sync. The right answer depends on shot type — the canvas lets you fan out and pick.
Do I need a custom-trained model on my character?
No. Reference-driven video skips the training step. A clean canonical still, refined on Nano Banana 2 if needed, anchors every video shot. LoRA-style fine-tuning is still possible for advanced cases, but the reference-driven canvas covers most series-grade work without it.
Can I use this for talking-head and dialogue?
Yes. Generate the consistent video shot first, then chain into a lip-sync node with ElevenLabs voice. The two-step chain — Vidu or Kling for the visual, lip-sync for the dialogue — produces cleaner results than asking a single model to do both.
How is this different from HeyGen or Synthesia?
HeyGen and Synthesia ship locked-down avatar tooling — useful but constrained to their library and their style. Martini gives you any character, anchored on the canvas, fanned across multiple best-in-class video engines, with full chaining into image refinement, lip-sync, audio, and NLE export. The character becomes a reusable asset across modalities, not a profile inside one tool.
How long can a single character video clip be?
Each engine has its own duration range. For series-grade character work, plan for shorter cuts (a few seconds each) chained on the canvas rather than long single takes — the character holds identity better across short anchored shots than across one long generation.
Build it on the canvas
Open Martini and wire this workflow up in minutes. Free to start — no card required.