Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration

🍞 Hook: You know how when you ask a friend to draw a 'cozy winter cabin,' you might picture warm lights and soft snow, but they might draw a bland house with no feeling? Your idea and their picture don’t match. That mismatch happens with AI, too.

🥬 The Concept (Generative AI): What it is: Generative AI is a set of computer models that can create new things like images, videos, music, and stories from examples they’ve learned. How it works: 1) Learn patterns from huge amounts of data. 2) Get a prompt like 'a cat on a skateboard.' 3) Predict and assemble pixels or words to match the prompt. 4) Output a new image or video. Why it matters: Without it, computers couldn’t make new creative content on demand. 🍞 Anchor: When you type 'a dragon flying over a city at sunset' into an image generator and get a brand-new picture, that’s Generative AI.

🍞 Hook: Imagine building a school play: someone writes the script, someone designs costumes, someone runs lights, and someone directs. That whole process is content creation.

🥬 The Concept (Content Creation Workflow): What it is: A content creation workflow is the set of steps people take to turn an idea into a finished piece, like a video or poster. How it works: 1) Set the goal and mood. 2) Plan the steps (script, storyboard, assets). 3) Produce parts (images, clips, audio). 4) Edit and assemble. 5) Review and fix. Why it matters: Without a workflow, projects fall apart or look inconsistent. 🍞 Anchor: A YouTuber planning their video outline, recording clips, adding music, and then editing is following a content creation workflow.

🍞 Hook: Have you ever tried super hard to explain a game you imagined, but your friend kept playing it the wrong way? That’s frustrating!

🥬 The Concept (Intent-Execution Gap): What it is: The Intent-Execution Gap is the difference between what a creator really wants and what the AI actually makes. How it works: 1) The user gives a short prompt. 2) The big model guesses a result in one shot. 3) The result is high quality but often misses tone, consistency, or logic. 4) The user retries with many tiny prompt tweaks. Why it matters: This trial-and-error loop wastes time, money, and energy, and blocks pros from using AI reliably. 🍞 Anchor: Asking for ‘a warm, nostalgic family video’ and getting random smiles with wrong outfits and choppy pacing shows the Intent-Execution Gap.

Before this paper, the world of generative AI was mostly 'model-centric.' People believed bigger models plus more data would fix everything. Video models improved a lot—sharper images, smoother motion—but creators hit a 'usability ceiling.' If a single video clip got the uniform wrong or the style drifted between scenes, the only fix was to re-roll and pray.

Folks tried several paths: 1) Prompt engineering: crafting long, detailed prompts. It helped a bit but still felt like fishing in the dark. 2) Reference-based generation and editing: using masks, style references, or subject personalization. Powerful, but data-hungry and still error-prone ('content leakage,' misalignment). 3) Unified multimodal models: one giant system for understanding and generating. Convenient, yet still lagging behind specialized video generators in quality and control.

What was missing? A way to turn fuzzy human ideas into a reliable plan with checks along the way. In real studios, teams don’t just roll a die for a whole movie in one go—they plan, divide tasks, and review.

🍞 Hook: Imagine a conductor leading an orchestra. Each musician is great at their instrument, but without a conductor, it’s just noise.

🥬 The Concept (Agentic Orchestration): What it is: Agentic orchestration is when many specialized AI helpers (agents) are coordinated to follow a plan and build complex content together. How it works: 1) Understand the high-level goal. 2) Break it into smaller tasks (script, shots, characters). 3) Assign each task to the best agent. 4) Check results and loop back if off-vibe. Why it matters: Without orchestration, even great agents can pull in different directions, causing messy results. 🍞 Anchor: For a music video, one agent analyzes beats, another writes scenes, another keeps character faces consistent, and a final one edits to the rhythm.

🍞 Hook: Think of switching from speaking 'robot code' to just telling a teammate, 'Make it feel like a calm summer evening.'

🥬 The Concept (Vibe Coding): What it is: Vibe Coding means giving high-level, feeling-based instructions (the vibe) that the AI can keep in mind over time while it plans and builds. How it works: 1) You describe mood, style, and goals. 2) The system holds that as a continuous 'project state.' 3) It makes choices (tools, styles) that fit the vibe. 4) It updates the plan when you give feedback. Why it matters: Without vibe-aware memory, AI treats each request as isolated and forgets the bigger picture. 🍞 Anchor: Saying 'dreamy pastel poster with playful fonts' and watching the system pick colors, type, and layout that all match that dream.

The gap this paper fills is shifting from 'make it all at once' guessing to 'plan it and prove it' teamwork. The stakes are real: classrooms needing consistent educational videos, small businesses wanting on-brand ads, families preserving memories correctly, and creators making long films without endless retries. By helping AI plan like a studio, we move from lucky hits to dependable craft.

The 'Aha!' in one sentence: Stop trying to make one giant model guess everything at once; instead, turn the user’s high-level vibe into a logical, multi-agent plan that can be checked and improved step by step.

Three analogies:

1. Movie studio: The user is the executive producer setting the feel; the Meta Planner is the director creating a shot list; specialized agents are the crew (writer, cinematographer, editor). 2) Kitchen: The user describes the feast’s mood; the Meta Planner writes the menu and timings; each cook focuses on dishes; tasting adjusts seasoning before serving. 3) LEGO city: The user says 'a cozy, green city'; the Planner picks sets and instructions; builders assemble neighborhoods; inspectors fix mismatched pieces.

Before vs After:

• Before: One-shot guesses, prompt fiddling, style drift, and expensive reruns. - After: A central planner expands the vibe into tasks for agents, uses knowledge to set constraints, verifies parts, and loops until it matches the commander’s intent. - Result: Fewer wasted generations, more consistency over long timelines.

Why it works (intuition):

• Human creativity is hierarchical (theme → scenes → shots → frames). Orchestration mirrors that structure. - 'Vibe' adds a persistent north star so local choices don’t wander. - A knowledge base translates fuzzy language into concrete knobs (e.g., 'oppressive' → low-key lighting, tight close-ups, low saturation). - Feedback loops catch errors early before they multiply.

Building blocks, each with a quick 'sandwich':

🍞 Hook: You know how setting the mood for a party guides all choices—music, lights, snacks? 🥬 The Concept (The Vibe): What it is: The vibe is a high-level bundle of style, mood, and goals the system remembers. How it works: 1) You describe feel + constraints. 2) System stores it as project state. 3) All agents consult it before acting. 4) Feedback updates the vibe. Why it matters: Without a steady mood, results wobble and feel random. 🍞 Anchor: 'Cozy bookstore ad' leads to warm colors, soft music, and close-up shots of pages.

🍞 Hook: Imagine a teacher who hears your idea and writes a plan the whole class can follow. 🥬 The Concept (Meta Planner): What it is: The Meta Planner is the system architect that turns your vibe into a step-by-step, multi-agent workflow. How it works: 1) Parse intent. 2) Consult knowledge to expand hints into rules. 3) Break tasks and assign tools. 4) Verify and adjust. Why it matters: Without a planner, agents pull apart, and projects fall off-track. 🍞 Anchor: For 'Hitchcockian suspense,' it schedules dolly zooms, sharp light contrasts, and tense cuts.

🍞 Hook: Think of spinning a big goal into smaller to-dos on a checklist. 🥬 The Concept (Hierarchical Orchestration): What it is: A top-down approach that maps big creative goals into layers of smaller, checkable tasks. How it works: 1) Make a macro blueprint. 2) Derive tool steps. 3) Set handoffs and checks. 4) Merge results. Why it matters: Without layers, you can’t control details or keep long stories consistent. 🍞 Anchor: A song-length music video is split into intro/verse/chorus scenes with matched beats and visuals.

🍞 Hook: Like having a shelf of recipe books and pro tips you consult while cooking. 🥬 The Concept (Domain Knowledge Base): What it is: A library of expert rules (film, design, audio) that transforms fuzzy words into concrete settings. How it works: 1) Query by style words. 2) Retrieve best practices. 3) Map to parameters. 4) Feed steps to agents. Why it matters: Without it, the system guesses and drifts. 🍞 Anchor: 'Film noir' pulls low-key lighting, venetian blind shadows, and moody jazz.

🍞 Hook: Picture a coach who checks each drill before game day. 🥬 The Concept (Human-in-the-Loop Feedback): What it is: The commander gives high-level notes to steer the plan, not every tiny knob. How it works: 1) Show previews. 2) Ask for mood-level feedback. 3) Update plan, not just the seed. 4) Re-run focused steps. Why it matters: Without guidance, the system can’t learn your taste. 🍞 Anchor: 'Make it darker and slower' adjusts lighting profiles and cut rhythm globally.

The core idea—Vibe AIGC—ties these blocks so the AI behaves less like a slot machine and more like a dependable creative team guided by your vibe.

At a high level: Input (your vibe) → Meta Planner (understand + expand) → Knowledge Base (make fuzzy concrete) → Hierarchical Orchestration (build workflow) → Agents execute (specialized tools) → Verification + Feedback (check and fix) → Output (consistent content).

Step-by-step, like a recipe, with examples:

1. Gather the Vibe

• What happens: You describe the project: 'A moody, rainy-night short film about a quiet hero, 2 minutes, blue-gray tones, slow pacing, subtle piano.' - Why it exists: This locks in style and goals the system can remember across many steps. Without it, each agent would guess its own style. - Example data: {mood: 'moody', palette: 'blue-gray', tempo: 'slow', music: 'piano', duration: 120s}.

1. Meta Planner: Intent Parsing

• What happens: The planner extracts explicit needs (duration, characters) and latent signals (tension, restraint). - Why it exists: To avoid missing hidden expectations. Without parsing, later steps won’t know the project’s heartbeat. - Example: It flags 'quiet hero' → minimal dialogue; 'rainy-night' → reflections, water sound design.

🍞 Hook: Imagine a coach turning 'play smart defense' into drills you can actually practice. 🥬 The Concept (Meta Planner – deep dive): What it is: A reasoning engine that converts vibe to system architecture. How it works: 1) Read instructions. 2) Infer hidden constraints. 3) Draft a plan. 4) Keep revising as feedback comes. Why it matters: Without this brain, parts don’t add up to the whole. 🍞 Anchor: It turns 'subtle piano' into timing markers for scene transitions.

1. Consult Domain Knowledge Base

• What happens: The planner asks the knowledge base how to express 'moody rain' visually and sonically. - Why it exists: Experts’ rules reduce guesswork. Without it, outputs may look generic. - Example: 'Moody rain' → low-key lighting, specular highlights, close-ups of droplets, low-saturation grade, sparse piano chords.

🍞 Hook: Like checking a style guide before designing a poster. 🥬 The Concept (Domain Knowledge Base – deep dive): What it is: A curated library of film, design, and audio best practices. How it works: 1) Match vibe terms to rules. 2) Pull parameter templates. 3) Bind to tools. 4) Return constraints to planner. Why it matters: It anchors taste words to technical choices. 🍞 Anchor: 'Slow pacing' → shot length 5–8s; 'blue-gray' → LUT preset BG-07.

1. Hierarchical Orchestration: Build the Blueprint

• What happens: The planner drafts layers: Creative (script beats) → Algorithmic (tools/agents) → Execution (configs). - Why it exists: Breaking complexity into layers ensures control and clarity. Without layers, errors hide and multiply. - Example creative layer: scenes = [intro under rain, reflection moment, reveal alley, quiet exit]. - Algorithmic layer: [script agent → storyboard agent → character bank → T2I keyframes → I2V motion → interpolation → color grade → audio mix].

🍞 Hook: Think of splitting a big school project into research, writing, and visuals with checkpoints. 🥬 The Concept (Hierarchical Orchestration – deep dive): What it is: A top-down decomposition that keeps the project aligned. How it works: 1) Make macro SOP. 2) Map to workflow graph. 3) Assign agents + data handoffs. 4) Schedule checks. Why it matters: It keeps long stories coherent. 🍞 Anchor: Every chorus in a music video uses the same character bank and lighting profile.

1. Agent Selection and Configuration

• What happens: The planner picks agents and sets knobs (sampling steps, guidance, masks). - Why it exists: Right tool, right settings. Without this, mismatches cause artifacts. - Example: Use a character-consistency agent with a shared embedding; use a reference color grade for all shots; set frame interpolation to x2 for smoother motion.

1. Execution with Checkpoints

• What happens: Agents run in stages, with previews for each milestone. - Why it exists: Early checks prevent costly re-renders. Without checkpoints, you only see problems at the end. - Example: After keyframes, the system shows thumbnails; you approve or ask for 'more reflective streets.'

1. Verification and Human-in-the-Loop Feedback

• What happens: The system compares outputs to the vibe (style metrics, beat alignment, identity checks) and asks you for mood-level notes. - Why it exists: Style is subjective; humans steer taste, machines handle mechanics. Without it, results creep off-vibe. - Example: You say 'Increase tension before the reveal' → Planner shortens shots, raises dissonant notes, tightens crop.

🍞 Hook: Like giving a coach note: 'More defense before halftime,' and the team changes formation. 🥬 The Concept (Human-in-the-Loop): What it is: Strategic feedback from the commander to adjust the plan, not just random seeds. How it works: 1) Show drafts. 2) Gather vibe-level notes. 3) Update blueprint. 4) Re-run targeted steps. Why it matters: It keeps control without micromanaging. 🍞 Anchor: 'Make it colder' updates LUT and lighting across scenes.

1. Final Assembly and Delivery

• What happens: The system stitches, color grades, mixes audio, upsamples, and exports. - Why it exists: A polished finish unifies parts. Without consistent finishing, the project feels patchy. - Example: Export 4K, 24fps, with consistent LUT BG-07 and final dynamic range checks.

The Secret Sauce:

• Persistent vibe state guiding every step. - Planner that reasons, not just routes. - Knowledge that turns poetic notes into technical parameters. - Layered checks to catch drift early. Together, they trade 'random re-rolls' for 'reliable revisions.'

What did they test? Because this is a paradigm paper, the 'tests' are real-world style case studies showing whether agentic orchestration beats single-shot generation for complex, long tasks.

• Text domain (AutoPR): Can agents turn a dense research paper into accurate, platform-tailored public posts with charts and figures—without the author juggling many tools? - Image/design domain (Poster Copilot): Can agents turn 'make it feel bold and balanced' into actual layout decisions (grid, typography, color) and let a human steer at a high level? - Video/music domain (AutoMV): Can agents keep a full song’s visuals consistent with beats, lyrics, characters, and mood, instead of generating disjointed 5–10 second clips?

Who/what was it compared against? Informally, each system was contrasted with the typical model-centric workflow: single prompts or a small chain of ad-hoc tools, lots of retries, and manual stitching.

The scoreboard, with context:

• AutoPR: Like going from copying bits into many apps to a 'one-click campaign.' Compared to solo prompting, it preserves technical details from PDFs, respects platform vibe differences, and reduces manual busywork. - Poster Copilot: Moves from 'random pretty poster' to 'designer’s assistant' that respects alignment, contrast, and hierarchy. That’s like upgrading from a B- collage to an A design that actually follows layout rules. - AutoMV: Instead of clip-by-clip roulette, the system keeps character faces stable, matches cuts to beats, and keeps the color mood across verses—a big leap in narrative and stylistic continuity.

Surprising findings:

1. Vibe words become more useful when tied to a knowledge base. 'Moody' stopped being a vague wish and started meaning specific lighting, lensing, and timing choices. 2) Feedback works best at the plan level, not the pixel level. Saying 'raise tension' triggered camera, music, and pacing changes together. 3) Smaller, specialized agents mattered. A 'character consistency' agent plus a 'beat alignment' agent together beat a larger, general model acting alone. 4) Checkpoints prevented waste. Early previews caught issues like wrong wardrobe or off-tempo cuts before full renders.

What’s missing? Formal numbers. The paper argues that existing metrics (like FID or generic text-image scores) don’t capture 'intent consistency' across time and tasks. Instead, it calls for new benchmarks: can a system take an ambiguous vibe and repeatedly turn it into a coherent, multi-step, multi-modal plan that holds together?

Bottom line: Across text, design, and music video cases, agentic orchestration reduced trial-and-error, boosted consistency, and made high-level feedback actually move the whole project in the right direction, even without a single 'do-it-all' mega-model.

Limitations (honest look):

• The Bitter Lesson possibility: If future foundation models get so good they fully internalize world knowledge and style rules, maybe one-shot prompts will be enough, shrinking the need for orchestration. - Control trade-off: Pros sometimes want pixel-perfect edits. High-level vibes can feel like a 'black box of intent' that smooths individuality. - Verification trouble: Code has unit tests; vibes do not. There’s no absolute 'pass/fail' for 'cinematic melancholy,' so convergence can be squishy. - Error stacking: Multi-agent chains can compound small mistakes (a misread costume note becoming a style mismatch across scenes) without an 'aesthetic compiler' to halt and diagnose.

Required resources:

• A good domain knowledge base (film/design/audio rules), curated or learned. - A library of reliable agents (script, storyboard, character, color grading, beat alignment, etc.). - A planner capable of reasoning and building workflow graphs. - Compute for iterative previews and refinements. - Interfaces for human-in-the-loop notes at the vibe/plan level.

When not to use it:

• Tiny tasks where a single prompt suffices (e.g., 'a red apple photo'). - Time-critical situations with no room for iterations. - Projects demanding hand-drawn, highly idiosyncratic styles where the artist wants full manual control of every stroke. - Settings without enough tools or knowledge to ground the vibe (you’ll get vague outputs).

Open questions:

• How do we score 'intent consistency' fairly across different tastes? - What does an 'aesthetic compiler' look like—what rules can be checked automatically? - How to balance Commander-level control with optional pixel-level overrides elegantly? - What standards let agents from different teams share memory (character banks, style states) safely and portably? - How to build datasets that map vibes → reasoning steps → agent graphs without massive manual labeling?

Three-sentence summary: This paper proposes Vibe AIGC, where users give a high-level vibe and a Meta Planner turns it into a verified, multi-agent workflow, replacing one-shot guessing with logical orchestration. By anchoring fuzzy styles to a domain knowledge base and layering plans into checkable steps, it bridges the Intent-Execution Gap for complex, long-form content. Early case studies suggest more consistency, fewer retries, and better alignment with human intent.

Main achievement: A clear top-level architecture—vibe state, Meta Planner, knowledge grounding, hierarchical orchestration, and feedback loops—that reframes content generation as system engineering rather than lucky sampling.

Future directions: Create intent-consistency benchmarks and 'creative unit tests,' train lightweight specialist agents, standardize agent protocols for shared memories and styles, and curate datasets that map vibes to stepwise workflows. Explore 'aesthetic compiler' ideas to catch and explain stylistic or narrative errors before they spread.

Why remember this: It marks a mindset shift—from bigger black boxes to smarter coordination—so AI becomes a trustworthy creative teammate. If widely adopted, it could democratize studio-quality production, letting more people command complex stories, songs, and visuals without drowning in prompt roulette.