What Is AIGC Video Production? How AI-Generated Video Is Made

AIGC (AI-generated content) is content — video, image, voice or text — produced or substantially transformed by a generative model rather than captured directly on camera. Some workflows begin from text prompts, while others use reference images, existing footage or scripts. The term can describe everything from a five-second generated clip to a complete commercial. For business buyers, it is useful to separate AI-generated asset creation from full AIGC video production: production includes the decisions and finishing work that turn generated assets into something ready to publish, which is why buyers comparing AIGC video production providers look past raw model output to the surrounding workflow. Lifewood positions its own AIGC video production offering the same way: as a managed process rather than a tool license.

Who is the audience? What should they learn or feel? What action should they take? Where will the content run? What does the brand need to protect? A strong AIGC script also respects the technology: it may avoid overly complex interactions that are difficult to generate consistently and instead use shots where AI can add real visual value. Teams that document this discipline as a brief an AIGC team can produce from tend to need fewer regeneration cycles later.

Generative systems produce many possible visual answers. Storyboards reduce that uncertainty by defining what each shot needs to accomplish. Styleframes go further by locking the intended appearance before motion generation starts, covering:

• Character appearance and wardrobe.
• Product shape and branding.
• Color palette and lighting.
• Camera angle and lens language.
• Environment and set design.
• Typography and graphic style.

These references become a visual contract between the creative team and client. They also make regeneration more efficient because the team is adjusting motion rather than re-deciding the entire art direction for every shot — the same discipline covered in AI storyboarding and previsualization.

Text-to-video is a generation method that produces moving video largely from a natural-language prompt, with no required visual reference. It is useful for ideation, atmospheric scenes, transitions and visual concepts where strict identity control is not the main requirement. The challenge is variability: small prompt changes can produce different framing, characters or details, making text-to-video powerful for exploration but sometimes harder for branded product or recurring-character work.

Image-to-video is a generation method that uses a supplied still image as the starting frame or composition, then generates motion around it. That makes it useful when the first frame, product, character or overall composition needs to remain controlled. Adobe's Firefly Video Model supports text-based and image-driven generative video workflows and is integrated into a broader multimodal creative environment. Adobe Firefly Video Model Professional teams often create a strong still image first, approve it, then animate it — a two-step process that can improve consistency because the design is locked before motion is introduced.

Character consistency is the degree to which a generated character's face, wardrobe and proportions stay identical across separate shots and prompts. It is one of the most difficult AIGC production problems: a face can change subtly between shots, hair or wardrobe can drift and body proportions may vary. Production teams typically:

• Create approved character reference images.
• Use the same visual vocabulary across prompts.
• Use identity/reference controls where available.
• Generate multiple takes and select compatible shots.
• Retouch or composite inconsistent details in post.
• Keep a reusable asset library for recurring campaigns.

The same principle applies to products: if exact product design matters, the team may combine real product renders or photography with generated environments rather than asking the model to recreate the product from memory. Brands that formalize this into repeatable rules for keeping AI-generated images on-brand see fewer inconsistent takes reach review.

The technical speed is useful, but human review remains important for pronunciation, emotion, pacing and consent. Voice is also a legal and reputational issue: brands should document whose voice is being used, whether cloning is authorized and how voice assets can be reused, which is the same governance question covered in multilingual AI voice production.

Editors select the best takes, build the story, adjust timing, and add transitions, VFX, graphics, color, sound design, music, subtitles and legal or brand elements. Tool's published AI-commercial making-of shows a multidisciplinary team including editors, CGI/VFX artists, AI specialists, music and sound, illustrating why finished AI video remains a production craft. Tool making-of

Text may be distorted, product features may change, a scene may imply something the brand did not intend, or a voice may pronounce a name incorrectly. A full pass typically covers visual artifact review, character and product continuity review, brand and tone review, factual and claims review, rights and source-asset review, localization and pronunciation review, and technical delivery review — the same checklist described in human-in-the-loop review of AI content. Human review is especially important for high-visibility brand content because the cost of publishing an incorrect or off-brand video is much higher than the cost of one more revision.

Teams often need vertical, square and horizontal versions, short cutdowns, subtitles and market-specific variants.

A strong production workflow plans these versions at storyboard stage. If the hero composition only works in widescreen, the vertical version may feel like a crop rather than an intentional piece of content. Enterprises publishing across many markets tend to plan this alongside a broader AIGC services engagement so localization is scoped from the start rather than bolted on afterward.