Define the boundaries of interface authenticity during project initiation
Before launching an AIGC commercial or AI product video project, brands must define how software interfaces are presented. Generative imagery excels at creating atmosphere and concepts but carries hallucination risks when handling precise user interface UI. If the video’s core selling point is demonstrating specific operational workflows, the team must confirm early on whether to use real screen recordings as the base. If relying entirely on AI-generated interfaces, one must accept potential defects like garbled text or layout misalignment and reserve time budgets for manual post-production corrections. Brands should provide high-fidelity prototypes or test environment accounts, not just concept sketches. This step determines whether subsequent production leans toward visual creativity or functional verification, which differ significantly in resource investment.
Storyboard scripts must mark interaction logic nodes
Traditional video scripts focus on dialogue and visual mood, whereas storyboards for AI product videos must include interaction logic descriptions. Every frame involving interface changes must note trigger conditions, navigation paths, and expected text content. Directors and editors must jointly review storyboards to ensure visual rhythm does not sacrifice information clarity. For example, when the voiceover mentions a smart analysis feature, the cursor movement trajectory and data chart changes on screen must synchronize. If storyboards lack these details, sound-picture desynchronization or functional misleading easily occurs during shooting or generation. It is recommended to add a remarks column next to storyboards to specifically record key states of UI elements, such as loading animations, pop-up positions, and button feedback effects, serving as hard constraints for post-production compositing.
Staffing and preliminary material preparation
Efficient execution relies on close collaboration across cross-functional teams. The project group must include product managers familiar with product logic, UI designers proficient in visual standards, and video producers skilled in AIGC tools. Product managers are responsible for outlining core function paths to ensure demo workflows align with current version logic. UI designers must provide vector-format interface component libraries, including standard color values, font files, and icon source files, for precise replacement or repair in post-production. Video producers must test the compatibility of various generative models with specific UI elements in advance and build local asset libraries. All participants should align their understanding of the weight between “authenticity” and “artistry” at the kickoff meeting to avoid disputes due to inconsistent standards later.
Separate live-action shooting from screen content during execution
For scenes involving humans operating devices, it is recommended to shoot actor actions against green screens or solid-color backgrounds, leaving screen content for post-production compositing. This separation strategy maximizes the clarity and accuracy of interface text. If shooting real screens on-site is necessary, pay attention to moiré patterns and environmental light reflection, which increase post-production repair difficulty. When using AIGC tools to generate backgrounds or auxiliary elements, lock camera position and focal length to avoid dynamic blur causing interface edge distortion. The production team must preview compositing effects on on-set monitors to confirm the match between actors’ gaze and virtual interfaces. Any improvisation deviating from preset angles may make it impossible to unify UI layer judgment in post-production, leading to rework.
Establish multi-layer verification mechanisms in post-production compositing
In the post-production phase, editors should manage interface assets independently from the main video track. For AI-generated background or effect layers, strictly isolate UI areas using masking techniques to prevent generative noise from eroding text edges. Color grading should avoid applying overly stylized filters to interface areas to prevent altering brand standard color values or reducing text contrast. Sound design should coordinate with interface interaction sound effects to enhance the realism of operational feedback. At this stage, product managers or UI designers should intervene to review and verify the accuracy of key operational steps frame by frame. If logical errors are found in AI-generated interface elements, such as inverted menu hierarchies or confused icon meanings, they must be replaced using traditional CGI methods rather than forcing the use of original generated results.
Acceptance checklists focus on compliance and clarity
Pre-delivery acceptance should not only focus on visual aesthetics but also establish a functional inspection checklist. First, verify that all interface text is spelled correctly, fonts comply with brand standards, and font sizes are readable on small-screen devices. Second, validate that the function demo workflow matches the currently live version to avoid legal risks from promoting unreleased or discontinued features. Also check compression performance at different resolutions to ensure fine coding artifacts do not affect critical information recognition. Regarding copyright, confirm that fonts, icons, and background music used have obtained commercial licenses, especially for materials generated by AIGC tools, where training data sources and commercial license terms must be verified. Each acceptance result should be documented in writing as the basis for final payment.
Identify project types unsuitable for AIGC
Not all AI product videos are suitable for generative workflows. If a project requires extremely high-precision demonstrations of medical, financial, or industrial software operations with zero tolerance for error, traditional screen recording with narration is more reliable. AIGC is better suited for concept teasers, brand image building, or abstract function visualization. When clients need to show specific coding processes, complex data backend logic, or strict compliance approval interfaces, the uncertainty of generative imagery may bring uncontrollable compliance hazards. Additionally, if brands cannot provide clear UI design specifications or test environments, forcibly using AI to generate interfaces will waste significant time on error correction rather than creativity. Teams should clearly inform clients of these limitations before quoting to manage expectations.
Prudent suggestions for next-step collaboration
Facing rapidly iterating AI technology, brands need not rush to completely replace existing production workflows. It is recommended to start with short social media videos or concept trailers to test the actual efficacy of AIGC in the workflow. Choose partners with complete process service capabilities from pre-production to delivery to ensure quality control by professionals from strategic planning to final delivery. Based on clear communication goals and audience needs, gradually explore the unique value of generative imagery in brand storytelling. Maintain a clear understanding of technical boundaries and concentrate resources on core content that best enhances user understanding and trust to achieve sustainable brand asset accumulation.
Build a standardized UI asset library
To avoid repeated communication on basic visual elements for each project, teams should build a standardized UI asset library. This library must include high-definition screenshots of common controls, motion graphic templates, and standard copy packages. The asset library should be updated regularly with product iterations to ensure materials remain synchronized with the online version. When producing AIGC commercials, prioritizing verified assets from the library can significantly reduce generation error rates. Newly generated interface elements, once reviewed and approved, should be promptly archived into the library to form a virtuous cycle. This not only improves efficiency per project but also ensures visual consistency of the brand across different channels.
Techniques for integrating dynamic lighting and shadows with interfaces
When AI-generated environmental lighting combines with static UIs, a sense of disconnection often arises. Post-production must integrate interfaces naturally into scenes by adding ambient occlusion, reflective highlights, and slight motion blur. If the interface is a pure flat asset, simulate corresponding brightness changes based on the scene’s light source direction. For example, in dim environments, the interface should appropriately lower brightness and increase diffuse reflection effects from surrounding objects. Technicians must manually adjust the blending mode of the interface layer to harmonize with the background tone. Avoid directly overlaying unprocessed UI layers, as this creates an obvious sticker-like effect, weakening the video’s realistic texture and immersive experience.
Adaptation strategies for multilingual versions
AIGC commercials targeting global markets must consider multilingual adaptation issues. Differences in text length across languages may disrupt original UI layouts. Reserve sufficient text expansion space during early design or adopt dynamic typography solutions. In post-production compositing, adjust interface element positions separately for each language to ensure key information is not obscured. If using AI for automatic translation of interface text, it must be proofread by native speakers to avoid ambiguity due to contextual differences. For right-to-left writing languages, mirror-flip the entire interface layout. Teams should establish multilingual production standards, specifying character spacing and line height standards for each language to guarantee reading experiences for global users.
Red lines for data security and privacy protection
When handling demo videos containing user data, strict adherence to data security regulations is mandatory. Never use real user information for demonstrations; all data must be anonymized or use fictional samples. If backend management systems need to be shown in videos, sensitive fields such as ID numbers and phone numbers must be masked. When using cloud-based AIGC tools, confirm the service provider’s data privacy policy to avoid uploading confidential code or core algorithm logic. Internal test environment accounts should follow the principle of least privilege to prevent system vulnerabilities from leaking due to video production needs. Teams must sign non-disclosure agreements and encrypt material transmission channels to ensure business secrets remain secure throughout the production cycle.
If you are preparing an AIGC commercial project, first organize the Brief, reference images, product or corporate materials, delivery platforms, and copyright scope, then view theAIGC Video Services pageto ground communication from abstract preferences to executable production boundaries.
