Assess the feasibility and core risks of initiating an AIGC commercial project
Before launching an AIGC commercial or AI product video project, marketing leads must validate the alignment of technical feasibility with business goals. Generative imagery is not a cheap substitute for traditional filming; its core value lies in achieving visual concepts difficult to attain through conventional means or significantly shortening production cycles for specific asset types. The initiation phase requires clarifying three criteria: whether the visuals heavily rely on real-world physical interactions, whether brand assets need pixel-perfect restoration, and whether the delivery timeline allows for model debugging iterations. If a project demands nuanced emotional micro-expressions from actors, strict industrial design tolerances for product appearance, or the delivery of untested new visual styles within tight deadlines, the risks of a pure AIGC approach should be carefully evaluated.
Brands must define the scope of copyright usage and compliance baselines for generative content in project initiation documents. Training data sources for mainstream generative models are complex, and commercial licensing terms change frequently with platform updates. Project teams should confirm the commercial licensing status of selected toolchains before signing contracts and allocate time for legal review. For shots involving faces, trademarks, or patented designs, manual review mechanisms must be established to avoid infringement or false advertising risks caused by AI hallucinations. Initiation documents should include backup traditional production plans, allowing a seamless switch to live-action or traditional CGI workflows if generative results fail to meet acceptance standards within the scheduled timeframe, ensuring overall project progress is not blocked by a single technical path.
Specifications for preparing materials to build a reusable prompt system
The quality ceiling of generative video depends on the structured nature of input materials, not merely on the rhetorical skill of prompts. Reference materials provided by brands should not be scattered mood boards but should be translated into machine-readable visual description documents. This includes converting brand color values into color space parameters, exporting product 3D models into standard formats with normal maps, and breaking down scene atmosphere into specific metrics for lighting direction and material reflectivity. Production teams must assist brands in this translation process, ensuring abstract brand tonality is anchored as reusable visual tags. For example, "high-end tech feel" should be defined as a parameter set combining cool-toned top lighting, low-saturation metallic textures, and shallow depth of field, rather than a vague pile of adjectives.
When building a prompt library, distinguish between global constraint terms and shot variable terms. Global constraint terms include fixed rules throughout the film, such as brand logo placement, product proportion relationships, and prohibited elements, which should be encapsulated as preset templates to avoid deviations from manual entry each time. Shot variable terms are designed for specific narrative needs, such as camera movement trajectories, focus changes, or character action amplitudes. The material preparation phase should also collect negative prompt samples, explicitly listing visual features that have historically led to generation failures or brand violations. This structured material serves not only as the execution basis for the current project but also as an important part of the brand's digital assets, providing a consistency benchmark for subsequent content production and reducing communication losses in cross-team collaboration.
Generative adaptation workflow from script to storyboard
Traditional film scripts focus on narrative logic and dialogue rhythm, whereas AIGC commercial scripts must simultaneously annotate generative technical parameters. Screenwriters and AI operators need to collaborate during the creation phase, breaking down each shot into executable generation instructions. In addition to conventional shot size, duration, and voiceover, script tables should add columns for prompt versions, reference frame numbers, ControlNet weights, and expected defect notes. This dual-track script forces creators to consider technical boundaries when conceiving visuals, avoiding complex interaction scenes that current models cannot stably generate. For example, shots with multiple characters in frame holding objects require planning layered generation or post-production compositing remedies at the script stage, rather than hoping for a perfect output from a single generation.
Storyboarding serves the dual function of visual pre-visualization and technical testing. It is recommended to use rapid generation tools to create dynamic storyboards to verify whether the actual effect of prompt combinations aligns with creative intentions. The key deliverables at this stage are parameter experiment records with timecodes. Each keyframe should be linked to its corresponding prompt version and seed value, forming a traceable debugging log. If a shot repeatedly fails to meet composition requirements after multiple generations, the script revision process should be triggered immediately to adjust the narrative approach or change visual metaphors, rather than indefinitely consuming compute power on futile attempts. Only after confirming the dynamic storyboard should the formal high-resolution generation phase begin, effectively controlling post-production rework costs and ensuring deviations between creative intent and technical implementation are identified and corrected early.
Parameter control and version management during the production execution phase
The "filming" of AIGC commercials is essentially a systematic exploration of the parameter space. Execution teams must establish strict version naming and archiving standards, automatically recording complete prompts, model versions, control signals, and random seeds for each generation result. Vague naming such as "final version" or "latest version" is prohibited; instead, use a structured naming convention with date, sequence number, and key parameter abbreviations. This engineering management ensures that any qualified frame can be precisely reproduced or fine-tuned, preventing loss of historical results due to environment changes or model updates. When batch-generating similar shots, lock the verified base parameter set and adjust only necessary variables to maintain visual style consistency.
Real-time quality check points must be set during on-site execution, rather than waiting until all materials are generated for unified screening. It is recommended to conduct sample reviews after completing each shot unit to confirm lighting continuity, object consistency, and brand element accuracy. When issues are found, prioritize tracing back to prompt structure or ControlNet settings, rather than simply increasing generation quantity to rely on luck. For sequences involving multi-shot transitions, plan generation strategies for transition frames in advance, using frame interpolation or style transfer techniques to smooth visual jumps. Execution teams should also retain original generation logs and intermediate files, as these materials have irreplaceable value for fixing errors or adjusting pacing in post-production. All generated materials should be stored by shot number and bidirectionally linked with script tables, ensuring editors can quickly locate usable clips and their technical parameter backgrounds.
Authenticity calibration and detail repair in post-production compositing
Once generative materials enter the post-production workflow, the primary task is to eliminate AI-specific visual artifacts and inject realism. Colorists must unify the color science of images generated in different batches, correcting color temperature drifts or contrast inconsistencies caused by model fluctuations. Sound design is particularly critical at this stage, as clean AI visuals often lack ambient sound beds and physical feedback effects, requiring auditory realism to be reconstructed through foley and spatial audio. Subtitle and graphic element layout should follow traditional film standards, avoiding direct overlay on generated images that causes visual fragmentation. For text errors or structural deformations in products, frame-by-frame repair or local inpainting techniques must be used; obvious details violating physical common sense must never be allowed to remain in the final cut.
Post-production compositing also assumes the role of secondary creation for narrative pacing. The original duration and motion curves of generative videos may not fit editing rhythms, requiring time perception to be reshaped through speed ramping, frame sampling, or cross-dissolves. Editors should focus on visual momentum connections between shots, using the plasticity of generative materials to compensate for transition effects difficult to achieve in live action. All post-production modifications should be documented, especially those involving brand element adjustments, which require written confirmation from the brand before proceeding. Multi-terminal adaptation tests should be conducted before master output to check for abnormal artifacts in generated textures under different compression bitrates. The source file delivery package should include layered projects, prompt documents, and repair logs, ensuring the brand can maintain or commission third parties to continue the project's visual system in the future.
Structured checklist and responsibility definition for final delivery acceptance
Acceptance of AIGC commercials cannot rely solely on subjective perception; it must verify objective indicators agreed upon during the initiation phase item by item. The acceptance checklist should cover five dimensions: brand asset accuracy, visual style consistency, technical parameter compliance rate, copyright compliance proof, and deliverable completeness. Brand asset accuracy refers to no deviation in hard metrics such as product shape, logo position, and standard color values; visual style consistency evaluates whether the tone, lighting, and materials remain unified throughout the film; technical parameter compliance rate checks whether resolution, frame rate, and bitrate meet the latest requirements of publishing platforms; copyright compliance proof requires providing commercial authorization credentials for used models and materials; deliverable completeness verifies whether masters, source files, prompt libraries, and documentation are complete. Failure to meet any dimension should be considered acceptance failure, not a minor flaw.
Issues found during acceptance must be handled according to severity levels. Fatal defects such as product errors, infringement risks, or major narrative loopholes must be reworked unconditionally; important defects such as local errors or sluggish pacing can be adjusted within agreed revision limits; minor defects such as blurred background details or texture flaws in non-core areas can be accepted or discounted by mutual agreement. All acceptance opinions should be recorded in writing to avoid misunderstandings from oral communication. Brands should sign off on results at each stage on the acceptance form; parts without objections are deemed approved and cannot serve as bases for rework later. This structured acceptance mechanism protects brand rights while preventing infinite revisions from consuming production resources, establishing predictable quality standards for the commercial delivery of AIGC projects.
Identifying applicable boundaries and restrained next-step recommendations
Although AIGC commercials show potential in concept visualization and efficiency improvement, their scope of application has clear boundaries. Current technology is not suitable for producing commercial content that heavily relies on the tension of human performance, requires precise physical simulation, or involves sensitive ethical issues. If brands pursue absolutely controllable product displays or strongly emotionally resonant storytelling, traditional live-action combined with CGI remains a safer choice. Generative imagery is more suitable for stylized promotional videos, abstract concept interpretations, massive material generation, or rapid prototype testing. Decision-makers should avoid viewing AIGC as a panacea, but rather position it as a professional tool within specific problem domains, matching the most suitable production path to the project's core demands.
If your brand is evaluating the applicability of AIGC commercials or AI product videos, it is recommended to start with a small-scale test project to verify the compatibility of the prompt system with internal approval processes. ONCE provides complete end-to-end services covering corporate videos, brand videos, TVC commercials, product videos, overseas marketing videos, social media short videos, and AIGC video production, assisting you through the entire process from project assessment to delivery acceptance. We recommend clarifying communication goals, audience personas, core selling points, and existing brand asset lists before contacting the production team, which will significantly improve early-stage communication efficiency. Technology iterates rapidly; specific platform specifications and compliance requirements should be based on the latest official information before release, and do not directly apply experiences from past cases to new project decisions.
If you are preparing an AIGC commercial project, you can first organize the brief, reference images, product or company materials, delivery platforms, and copyright scope, then view theAIGC Video Services page, to ground communication from abstract preferences to executable production boundaries.
