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The Complete Beginner Guide to Making AI Videos (Higgsfield, Seedance & Genjutsu)

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This comprehensive beginner guide details the end-to-end workflow for generating high-quality AI videos using platforms like Higgsfield and video models like Seedance 2.5. Creator Dan breaks down the critical importance of crafting reference assets—character sheets, cinematic locations, and object sheets—before generating expensive video footage. He demonstrates advanced prompting frameworks using custom LLM skills, explains draft resolution saving techniques, provides an audio matching workaround with MP4 references, and showcases motion transfer using Genjutsu.

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11chapters16on-screen captures3people9claims and stats13references in all
Source: How To Start Making AI Videos in 2026 (Beginner Guide) · Dan Kieft
Find in this video

Search what was said and shown

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    The explainer

    What the video says, with a link to every moment

    A 1-minute read, 26 min less than watching. Each underlined phrase opens the video at that second.

    Creating high-quality AI cinematic videos requires a disciplined pre-production workflow rather than relying on simple text prompts. In this tutorial, Dan establishes that starting directly with text-to-video typically wastes credits on unpredictable outputs. Instead, creators should implement a multi-stage production pipeline centered around reference assets and workflow planning2:43. Dan recommends using multi-model aggregator platforms like Higgsfield platform overview2:09 so users remain agile as underlying generative models continuously update.

    slide
    1. TEXT TO VIDEO2. REF TO VIDEO
    ▶ 2:43
    On screen at 2:43

    The foundation of consistency lies in generating detailed reference sheets. Dan demonstrates how to generate budget-friendly character sheets using AI Cast in Cinema Studio4:09, which costs as little as 0.125 credits compared to hundreds of credits for video runs. To gain maximum fidelity across three angles (front, back, and close-up face), he provides a three-panel character prompt formula5:26 executed through ChatGPT. The same principles apply to environments using cinematic location generation8:35 and props via multi-view product sheets10:18, where mid-gray backgrounds prevent overexposure artifacts in subsequent video generation.

    framework
    CHARACTER SHEET PROMPTA three-panel character reference sheet composed as one horizontal image, divided into three equal vertical panels side by side with thin clean separations. The same person in the same outfit, rendered identically in all three panels. No text, no labels, no numbering anywhere in the image.
    ▶ 5:23
    On screen at 5:23

    Moving into video generation, Dan prioritizes the Seedance 2.5 model and explains an exhaustive eight-part prompting framework spanning goal, references, scene setting, staged shot-by-shot actions, visual style, audio dialogue, and strict consistency locks during breakdown of the prompting framework12:31. To eliminate manual prompt writing, he walks through installing a custom LLM skill file14:42 inside ChatGPT and dictating ideas using Whisper Flow. Before committing heavy credit investments, he stresses rendering at draft mode resolution16:58 to catch narrative and spatial inconsistencies early.

    framework
    PROMPTING FORMAT[GOAL][CHARACTER AND REFERENCES][SCENE][STAGE 1 | 0-4 SECONDS | THE APPROACH][STAGE 2 | 4-6 SECONDS | THE FINAL STEPS][STAGE 3 | 6-8 SECONDS | THE REACH][STAGE 4 | 8-15 SECONDS | THE SHUTDOWN][VISUAL STYLE][AUDIO][MAINTAIN CONSISTENCY]
    ▶ 12:26
    On screen at 12:26

    Dan demonstrates the full power of multi-asset control in reference-to-video generation19:35, mapping character sheets, location images, and prop references to numbered IDs in a single cinematic scene. To resolve mismatched synthetic voices, he unveils a clever workaround: recording custom voice acting and uploading it as an MP4 audio reference file23:19, which forces the model to synchronize real human cadence and vocal timbre. Finally, Dan explores Genjutsu motion transfer24:39, showing how creators can map custom characters over real-world dance or acting footage to generate viral clips ethically without stealing other people's videos.

    Highlights
    1. 2:43Asset generation workflow and cost comparison
    2. 5:22Template and LLM prompting for character references
    3. 11:32AI video generation models and structured prompting
    4. 14:26Installing the custom Seedance prompting skill in ChatGPT
    5. 19:35Reference-to-video multi-asset crime scene workflow
    6. 24:30Motion transfer and viral meme generation with Genjutsu
    Chapters across the 26:42 runtime
    How it's told

    A hands-on tutorial framed by comedic sketches

    Format: Tutorial. Dan Kieft combines comedic sketches with live platform walkthroughs, prompt engineering formulas, and tool comparisons to guide beginners through consistent AI video generation.

    Story arc

    1. 0:00Comedy SketchFrustrated creator Dan receives the magical 'AI Blueprint' from his robot companion Bruiser.
    2. 1:16Introduction & OverviewOverview of what it takes to produce consistent cinematic AI videos and how to save credits.
    3. 1:31Step 1 - Selecting the Right Video ModelDan explains the criteria for choosing an aggregator platform like Higgsfield hosting multiple AI models.
    4. 2:39Step 2 - Asset Prompting GuideDan teaches creating character sheets, location sheets, and object turnarounds before generating video.
    5. 11:24Step 3 - How to Make AI VideosDan outlines structuring prompts with goals, character tags, scenes, stages, styles, and audio.
    6. 14:24Custom LLM Prompter SetupInstalling and using custom ChatGPT markdown skills with voice dictation for instant multi-shot prompts.
    7. 16:39Text-to-Video Workflow & Draft TestingGenerating a draft comedy scene, analyzing flaws, and iterating prompts in draft resolution.
    8. 19:35Reference-to-Video WorkflowLinking multiple reference assets and custom voice audio to produce consistent cinematic multi-shot scenes.
    9. 24:25Motion Transfer with GenjutsuExplaining viral video techniques using real footage and AI character swaps via Genjutsu.
    10. 26:05Conclusion & TakeawaysFinal advice on original content creation and a preview of advanced credit-saving techniques.

    Setups and payoffs

    1. 0:56Setup The mystical glowing 'AI Blueprint' book is opened by Dan
      ↳
      1:31Payoff The contents of the AI Blueprint are taught systematically across Steps 1, 2, and 3.
    2. 1:23Setup Preview clip of Dan and Bruiser doing an alleyway deal with gangsters
      ↳
      21:56Payoff Full generation and breakdown of the completed Kinder Surprise deal scene.
    3. 3:43Setup Dan warns that bad asset prompting leads to bad videos and wasted credits
      ↳
      4:47Payoff He demonstrates low-cost image generation before committing heavy credits to video rendering.
    4. 14:39Setup Dan introduces a custom prompter markdown file for ChatGPT
      ↳
      15:05Payoff He uses the installed prompter skill to build detailed multi-stage video scripts in seconds.
    5. 17:16Setup The initial draft video has awkward timing, sound issues, and unconvincing voices
      ↳
      23:58Payoff Adding custom voice audio files and reference assets fixes identity and voice consistency.

    Devices

    • SKETCH FRAMINGBookending the tutorial with a narrative comedy sketch involving a sentient domestic robot and contraband candy.0:0021:56
    • SCREEN CAST DEMOReal-time UI walkthroughs across Higgsfield, ChatGPT, and Audacity to demystify complex AI workflows.4:1214:4623:05
    • PROMPT BREAKDOWNOn-screen annotated breakdown color-coding Goal, Continuity, Stage Actions, Visual Style, and Audio.12:3014:15
    • SIDE-BY-SIDE COMPARISONDisplaying identical prompts across multiple competing image and video models to evaluate strengths.7:279:5111:0125:58

    From the video's own structure, matched to the transcript and on-screen text.

    Teardown

    An approachable thumbnail and promise of an updated 2026 beginner roadmap earn the click, while structured step-by-step asset workflows keep technical learners watching despite screencast pacing lags.

    Below the channel's typical: 0.7× its typical views (123,301 vs a median of 191,030 across the channel's last 15 long-form uploads, as of Nov 2) · Long-form tutorial · 10 days old

    The promise

    A complete, beginner-friendly walkthrough showing how to start creating AI videos from scratch in 2026.

    Packaging

    How it looks in the feed

    Show the feed as
    26:42

    How To Start Making AI Videos in 2026 (Beginner Guide)

    Dan Kieft123K views · 1w agoHow To Start Making AI Video in 2026 (Beginner Guide) Creat AI Video with Higgsfield 👉 http://dankieft.com/higgsfield All of my prompts…

    Views and age as of the teardown, Nov 2, 2026. Other videos are blanked out.

    • 4/5Thumbnail imageThe high-angle selfie shot with a rendered sci-fi robot immediately demonstrates the creative output while staying warm and approachable. The clean white backdrop ensures strong contrast in dark mode feeds.A creator taking a high-angle selfie with an orange and grey mechanical robot companion against a plain white background.
    • 4/5Thumbnail text“Start here.”At only two words, it immediately directs overwhelmed viewers looking for an entry point. It reduces friction rather than cluttering the frame.Purple serif text reading 'Start here.' positioned across the top third.
    • 3/5TitleIt is clear, search-friendly, and qualifies the audience directly. However, it relies heavily on standard SEO phrasing and lacks a unique curiosity gap.Standard tutorial phrasing 'How To Start Making AI Videos in 2026 (Beginner Guide)'.
    • 4/5Title + thumbnailThe thumbnail lowers anxiety with an inviting visual and simple directive, while the title gives the exact educational context. Together they present an accessible gateway into a complex topic.The image shows a friendly robot with 'Start here.', while the title explains it is an AI video beginner guide for 2026.

    Curiosity gap: How can a complete beginner actually produce high-quality character videos like the robot shown?

    The three hooks

    The title, the thumbnail text and the first words spoken each make a promise, and each does a different job.

    • Title“How To Start Making AI Videos in 2026 (Beginner Guide)”How-toOutcomeNoveltyIt makes the viewer want a current, step-by-step roadmap to start creating AI video content.
    • Thumbnail text“Start here.”ReliefReliefOutcomeIt makes the viewer want a simple, un-intimidating starting point in a confusing and fast-evolving field.
    • First words spoken“Nothing I make works. Everything I try fails.”ReliefReliefLoss aversionIt taps into creator despair over distorted, failed AI video generations so they want a reliable, step-by-step formula that actually works.
    • PairingDifferent jobsThe thumbnail offers an inviting, low-friction entry point, while the title supplies the technical subject and target audience.
    • StakesFitsThe video delivers an exhaustive 26-minute beginner roadmap that directly matches its packaging promise of showing how to create consistent AI videos without wasting credits.
    • UrgencyNoneThe packaging uses the year 2026 for relevance and search positioning rather than imposing artificial countdowns or manufactured scarcity.

    The first 20 seconds

    TimeWhat you seeWhat you hearWhat it does
    0:00Close-up shot of a hand clicking on a white computer mouse on a wooden desk.Upbeat, playful background music kicks off.Establishes an active creative workspace immediately.
    0:01Over-the-shoulder shot of a computer monitor displaying a 3D-rendered character with uncanny facial features in digital editing software.Playful rhythmic electronic music continues.Reveals the project: a strange, slightly uncanny animated character.
    0:02Tight close-up of a young man's eyes staring intensely and nervously at the screen.Music continues steady.Builds tension and shows frustration through intense eye contact.
    0:03Cut to a cute CGI robot wearing glasses sitting on a sofa reading a book titled 'How to Treat Your Human'.Music plays smoothly.Injects immediate surreal humor and high visual production value.
    0:04Robot tilts head slightly while reading; the camera holds on the robot.Music groove continues.Lets the comedic visual gag land with the audience.
    0:05Rapid close-up of fingers furiously typing on a slim white keyboard.Fast typing clicks layered over background music.Pumps up the pace and shows frantic effort.
    0:06Return to the robot turning a page in its book calmly.Music continues.Contrasts the creator's panic with the robot's calm humor.
    0:07Robot adjusts its position on the sofa, still reading.Music sustains beat.Maintains the ongoing comedic juxtaposition.
    0:08Close profile of the man biting his lip in stress while staring at the screen.A rising tense sound effect swells beneath the beat.Signals an imminent emotional breakdown.
    0:09He clenches his jaw, eyes wide with frustration.Riser sound reaches a peak, followed by a comical squeak.Snaps the tension into pure comedy.
    0:10Wide shot of the bright sunlit home office as the man throws his head back and groans in his chair.Music cuts to low ambient tone; man groans audibly.Releases the frustration in an exaggerated, relatable way.
    0:11He holds his head in his hands, slumped in the swivel chair.Chair creaks softly as he shifts.Dramatizes creative exhaustion.
    0:12He violently grabs his hair, stands up, and storms away from the desk.Rustle of clothes and rapid footsteps.Propels physical energy across the room.
    0:13He marches toward the living room sofa where the robot is reading.Heavy footfalls and frustrated sighs.Transitions the action from desk to character interaction.
    0:14He flops dramatically onto the couch next to the robot, sighing deeply.Exhaled groan: 'Ugh!' as couch cushions thud.Lands the physical comedy beside the CGI companion.
    0:15He rubs his forehead in despair while looking toward the robot. Subtitle appears: 'Nothing I make works.'Man speaks: 'Nothing I make works.'States the relatable creator pain point directly.
    0:16The robot lowers its book slightly and looks over sympathetically with digital eyes.Man continues: 'Everything I try... 'Humanizes the robot's emotional reaction.
    0:17Robot blinks animated heart-shaped or happy eyes as the man complains.Man finishes: '...fails.'Contrasts despair with sweet comedic AI support.
    0:18The man gestures angrily back toward his monitors across the room.Man shouts: 'Look at this thing!'Redirects attention back to the flawed digital creation.
    0:19Over-the-shoulder perspective from the robot looking at the man pointing at the screen.Man exclaims: 'It's a nightmare!'Caps the comedic scene on high emotion before transitioning into the solution.
    0:20Man holds his pointing gesture, visibly outraged by his render.Man sighs after shouting.Leaves the problem wide open for the tutorial to fix.
    Click confirmation
    Confirms the video subject instantly by showing 3D digital character rendering on a workstation screen within 00:01.
    Time to the point
    0:15
    Hook
    in the middle of the action, personal confession
    Reason to stay
    The creator is visibly failing to make decent AI/3D content, opening the loop of how to actually create polished AI video without it turning into a 'nightmare'.
    Missing on purpose
    Skips standard introductory channel greetings, logos, and sponsor shoutouts, launching straight into a high-production sketch.
    Pace and texture
    Cuts every 1 to 2 seconds between intense creator frustration and a calm, reading CGI robot, punctuated by dynamic sound effects and comedic pacing.

    Story map

    Tutorial. A creator frustrated by uncanny, failing AI generations wants to produce cinematic videos without burning expensive credits; by building multi-angle image assets and structured prompt templates first, he achieves controlled reference-to-video generation.

    TimeSectionNew tensionStakesTurnLoops
    0:00 –1:30Comedy Sketch & The AI Blueprint HookCan Dan stop creating uncanny, broken video renders?RiseHis domestic robot Bruiser unlocks a safe with 'The AI Blueprint', therefore Dan has a proven formula to reveal.Opens: What is inside the AI Blueprint that makes videos actually work?Closes: Why Dan was failing at his desk.
    1:30 –3:42Platform Selection & Asset-First PrincipleHow do you avoid wasting money on single AI models that quickly go obsolete?HoldText-to-video lacks consistency, therefore you must switch to an asset-first workflow before touching video generation.Opens: How do you generate consistent reference assets cheaply?Closes: Which platform setup to choose.
    3:42 –8:20Consistent Character SheetsHow do you prevent a character's face and clothes from morphing between shots?RiseSingle-angle prompts produce erratic results, therefore Dan uses an LLM prompt template to generate three-panel turnaround sheets.Opens: How do you maintain environment and prop consistency across shots?Closes: How to create character reference sheets for under 5 credits.
    8:20 –11:24Location & Prop TurnaroundsWill backgrounds and props warp when the camera moves?HoldWhite studio backgrounds overexpose AI video renders, therefore you must generate props on neutral gray backgrounds.Opens: How do you feed multiple assets into a single video prompt?Closes: Creating location and prop reference sheets.
    11:24 –14:24The Master Video Prompt FrameworkHow do you construct a video prompt that controls camera, character, and action at once?RiseWriting 8-part prompt architectures manually is overwhelming, therefore Dan shows how to offload prompt drafting to custom LLM skills.Opens: Does the automated ChatGPT skill actually output functional video prompts?Closes: The 8 core components of a cinematic AI prompt.
    14:24 –17:16LLM Voice Dictation & Draft TestingWill the automated prompt generate a clean scene on the first try?HoldThe first video render has timing flaws and awkward door physics, therefore Dan iterates using low-cost 480p draft mode.Opens: How do you refine a messy generation into a polished shot list?Closes: Setting up the custom ChatGPT prompter.
    17:16 –19:35Prompt Iteration BreakdownHow many iterations does it really take to clean up generative artifacts?FallSag: no turnOpens: When will we see the full multi-asset reference pipeline from the hook?Closes: Demonstrating negative prompt revision on a hangover scene.
    19:35 –22:45Reference-to-Video Multi-Asset GenerationCan Seedance handle six different asset references at once without blending identities?RiseThe scene generates with great visual consistency, but the voice acting sounds generic and like Vin Diesel.Opens: How do you fix bad AI voices without manually re-dubbing everything in an NLE?Closes: Generating a 30-second multi-character scene from asset sheets.
    22:45 –24:25Audio & Custom Voice Sync TrickHow do you lock an AI video's vocal performance to an exact cadence?HoldDirect audio files fail consistency checks, therefore Dan wraps voice recordings into black-screen MP4 videos as references.Opens: What other shortcuts exist for viral video styles?Closes: Fixing character vocal identity and sync.
    24:25 –26:42Genjutsu Motion Transfer & OutroAre viral AI character videos fully generated or motion captured?FallStealing footage is unethical, therefore film original actions on your phone and swap characters using Genjutsu motion extraction.Closes: How viral motion transfer works; beginner roadmap complete.

    Payoff: delivered fully at 21:56. The core promise is delivered when Dan successfully renders a 30-second multi-character crime scene by linking six distinct image references. Ending: Dan closes after demonstrating a motion-transfer trick with Genjutsu, warning against stealing viral clips, and pointing viewers to an end-screen video on saving platform credits.

    Leaks

    Where a viewer might leave. Guesses from watching, not retention data.

    • 2:08 Dan transitions into an extended sponsor pitch for Higgsfield, breaking the narrative momentum set up by the vault sketch.
    • 5:54 The pacing slows to a crawl as Dan copies, pastes, and reads prompt template text in ChatGPT rather than showing results.
    • 18:20 Dan scrolls through long chat logs detailing revisions for a secondary hangover scene that feels tangential to the main alleyway deal scene.
    • 24:25 The core reference-to-video promise has already paid off, making the Genjutsu motion-transfer segment feel like an optional add-on.

    Steal this

    1. Neutral-Gray Prop Turnarounds Prevents lighting blowouts and clipping when feeding object reference sheets into video generators. 10:40
    2. The Low-Res Draft Buffer Lowers production costs and anxiety by enforcing 480p testing for timing and blocking before committing expensive credits to 1080p upscales. 16:52
    3. Black-Screen MP4 Voice Forcing Bypasses buggy audio-only reference tools by uploading dialogue tracks wrapped inside blank video containers to lock vocal performance. 23:14
    4. Sketch-to-Asset Pipeline Framing Dramatizes common viewer pain points with live-action comedy, then repurposes the sketch props and characters as test subjects inside the tutorial. 0:15

    Skip this

    • Custom high-end CGI companion robot (Bruiser) Dan has pre-built 3D character pipeline assets that elevate his skits, which beginner creators cannot easily clone.
    • Carefree multi-model credit burning Dan's direct platform sponsorship allows him to run identical prompts across 4 paid models simultaneously, a luxury beginners paying out of pocket cannot afford.

    Open questions

    • What percentage of viewers dropped off during the extended ChatGPT prompt configuration at 14:40?
    • How many viewers downloaded Dan's custom markdown prompt skill from Skool versus writing their own?
    • Did viewer retention dip when Higgsfield was named as the exclusive sponsor at 02:08?

    Teardown by Avail.video from the thumbnail, the title, the first 20 seconds and the report's notes on the whole video.

    On screen

    Everything the video showed you

    Slides, frameworks, quotes and formulas, captured verbatim with the moment they appeared. Tap any card to jump there.

    Reference shelf

    People, ideas and things mentioned

    Said means mentioned aloud; Shown means it appeared on screen.

    People3

    • Dan KieftVideo creator, educator, and actor presenting the tutorial.0:00Shown1:16Said + shown1:34Said + shown2:42Said + shown3:50Said + shown
    • Vin DieselHollywood actor referenced for gangster voice acting comparison.22:31Shown22:32Said + shown
    • Jean PhilanthropeContent creator referenced for dance and motion replacement video formats.24:30Shown

    Things on screen10

    • BruiserDan's fictional domestic robot companion featured in sketch framing and reference video scenes.0:03Shown0:26Said1:07Said19:48Said + shown22:15Said
    • HiggsfieldAI creation platform integrating multiple image and video generative models, custom tools, and APIs.2:08Said + shown2:09Shown4:00Shown24:35Said + shown
    • Seedance 2.5AI video generation model offering high prompt adherence and multi-reference integration.11:43Said15:05Said + shown21:00Shown
    • GenjutsuHiggsfield native motion transfer tool that maps character references onto extracted video motion.2:20Said24:40Said + shown25:24Shown
    • Cinema StudioFeature suite within Higgsfield containing AI Cast and Cinematic Locations tools.4:02Said + shown8:30Said + shown
    • ChatGPTOpenAI conversational LLM used to expand and structure video prompt formulas.5:54Shown5:57Said + shown14:40Said + shown
    • Whisper FlowVoice-to-text dictation tool used to dictate scene narrative concepts directly into LLMs.6:12Said15:22Said15:23Shown
    • SeaDream 5.0 ProAI image generation model available on Higgsfield.7:07Said + shown7:20Shown11:08Shown
    • Nano Banana 2.1Image generation model on Higgsfield recommended for consistent object reference sheets.7:25Said + shown9:28Shown10:19Said + shown
    • Kinder SurpriseChocolate confectionery egg used as a contraband prop in Dan's demo video.19:47Shown20:10Said + shown22:21Said
    Video description

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    1,391 / 5,000 characters
    By the numbers

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    As of Nov 2, 2026. YouTube's public numbers first, then Avail.video's own arithmetic on them.

    The video

    Views
    123.3K
    Likes
    —
    Comments
    66
    Published
    Oct 22, 2026
    Length
    26:42
    Category
    Education
    YouTube's topics
    Lifestyle (sociology), Technology
    Captions
    No
    HD
    Yes
    Language
    —
    Paid promotion
    None declared
    Made for kids
    No
    License
    Standard YouTube

    Creator's tags

    No tags

    The channel

    Dan Kieft@dankieft
    Subscribers
    318K
    Videos
    230
    Total views
    23.9M
    Channel age
    3 years (since Aug 2023)
    Country
    —
    Uploads
    about every 5 days
    Across the last 15 long-form uploads
    Before this video
    6 days after the previous long-form upload

    Calculated by Avail.video from YouTube's public numbers

    Outlier score
    0.7× · Below typical
    Views against the median of the channel's last 15 long-form uploads
    Views per subscriber
    0.39
    Likes vs the channel's usual
    —
    Comments vs the channel's usual
    1.0× the channel's usual
    0.054% of views vs a usual 0.055%
    Rank among recent uploads
    #14 of 16
    By views: this video and the channel's last 15 long-form uploads
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