Define communication goals and asset boundaries during the project initiation phase.
Before launching an AIGC commercial project, brands must clarify core selling points and audience profiles. This step determines the direction of subsequent content generation. Marketing leads should provide clear brand guidelines, references to past high-quality cases, and a definitive list of prohibitions. Without specific references, generative models tend to produce visuals with style drift. Teams must define copyright scope in contracts and confirm whether training data involves third-party intellectual property risks. At this stage, do not discuss specific quotes or timelines; the focus is on confirming content compliance and creative feasibility. If the brand operates in a heavily regulated industry, such as healthcare or finance, introduce legal review mechanisms early to avoid having to restart later due to compliance issues.
Execution actions include building a brand visual asset library, digitally archiving logo specifications, standard color values, and font files. The criterion is whether all participants can access the latest brand guidelines within ten minutes. Simultaneously, sign data confidentiality agreements to ensure materials uploaded to generation platforms do not leak trade secrets. For sensitive industries, list prohibited vocabulary and image elements, such as prohibiting unverified efficacy claims in medical device visuals.
Control the level of detail in scripts and storyboards.
Traditional live-action relies on detailed storyboards, while AIGC projects emphasize prompt precision. Screenwriters and visual directors must jointly deconstruct the script, translating abstract concepts into visual shot descriptions. Each shot must specify shot size, lighting tone, subject action, and environmental details. Human review checkpoints are crucial here to check for ambiguity in prompts. For example, the term "modern" may render as minimalism or cyberpunk in different models, so reference images must be attached to lock the style. Teams should establish version control mechanisms to record the logic behind each prompt adjustment, facilitating traceability of visual changes. If storyboards are too brief, post-production will face extensive invalid generations, increasing computational costs and time loss.
Specific actions require each shot to include at least two style reference images and a descriptive text of no fewer than fifty words. The review standard is whether the prompt includes the four key elements: subject, environment, lighting, and composition. If vague adjectives appear in the description, they must be replaced with specific physical feature descriptions. The storyboard must note estimated shot duration to control the maximum frame count for generated videos later.
Verify style consistency in generative imagery.
The biggest challenge in AIGC commercials is maintaining consistency in characters and scenes. During batch generation, human review must focus on the continuity of the protagonist's facial features, clothing details, and environmental lighting. We recommend using fixed seed values or reference image functions to constrain generation results. Reviewers must check frame by frame for common artifacts such as limb distortion, finger anomalies, or background logical errors. Key close-up shots may require multiple iterations to obtain usable footage. Do not pursue one-shot perfection; instead, set a pass-rate threshold. If consecutive generations fail to meet the baseline, reassess whether the shot is suitable for AI implementation or consider hybrid production combining live-action footage.
At the execution level, establish character-specific LoRA models or embedding vectors to ensure consistent facial proportions for the same character across different shots. Criteria include ensuring that deviations in the protagonist's pupil color, hairstyle contour, and clothing texture across consecutive shots do not exceed five percent. Reviewers should use grid comparison methods, displaying newly generated frames side-by-side with baseline frames to quickly identify subtle differences.
Execute the integration of live-action and generated assets.
Purely generated videos often lack realistic texture; combining live-action elements enhances credibility. Production teams must plan which parts are captured by camera and which are generated by algorithms. On set, strictly match the lighting direction and color temperature of AI-generated backgrounds to enable seamless post-production compositing. Gaffers and cinematographers must light based on pre-generated background plates. Human review checkpoints include real-time comparison on on-set monitors to ensure the perspective of live-action subjects aligns with virtual backgrounds. If discrepancies are found, adjust camera positions or lighting on the spot rather than leaving it for post-production repair. This upfront control significantly reduces compositing difficulty, avoiding obvious edge cutting or lighting conflicts.
Specific actions involve outputting low-resolution background previews before shooting, printed or displayed next to on-set monitors. Gaffers must use colorimeters to measure on-set lighting, ensuring the color temperature error relative to the generated background remains within 200 Kelvin. The criterion is whether the shadow direction of live-action subjects aligns logically with background light sources and whether specular highlights blend naturally into the environment.
Reconstruct rhythm and narrative during post-production editing.
In the editing phase, the focus shifts from image generation to narrative pacing. Editors must connect scattered generated clips into a complete storyline. Since AI-generated footage has limited duration, shots often need extension through speed ramping, reversal, or looping. Reviewers must monitor the naturalness of action transitions to avoid abrupt jump cuts. Sound design intervenes at this stage, with sound effects and music compensating for insufficient visual dynamics. Human review must confirm audio-visual synchronization, especially lip-sync accuracy or the spatial sense of ambient sounds. If using AI-generated voiceovers, check whether emotional expression aligns with brand tonality, replacing with human dubbing if necessary. This stage requires outputting a rough cut for internal review to confirm narrative logic before proceeding to fine-tuning.
Execution actions include building a mapping table between sound effect libraries and visual actions, ensuring every step has corresponding auditory feedback. The criterion is whether viewers can understand the general plot progression by sound alone, without watching the screen. For lip-sync, use specialized lip-correction tools for fine-tuning to ensure mouth shape changes during vowel pronunciation correspond to audio waveforms.
Standardized acceptance for color grading and master delivery.
Final visual effects rely on a unified color grading style. Colorists must unify all assets to the brand-specified color space, eliminating color shifts between different generation batches. The acceptance checklist should include technical metrics such as resolution, frame rate, bitrate, and audio levels. Brands must confirm specifications based on platform requirements, noting differences in compression algorithms across platforms. Deliverables should include the final video, a version without subtitles, separated audio tracks, and project file backups. Reviewers must check shadow details and highlight clipping on professional monitors in a completely dark environment. If excessive noise or color banding is found, return to upstream stages to optimize source assets. Strictly prohibit external release before confirming copyright clearance, ensuring all generated elements have legal authorization or usage licenses.
Specific actions involve applying unified LUT presets and performing secondary color correction for each shot. Criteria include skin tone restoration accuracy, black level consistency, and whether overall contrast complies with brand visual standards. Before delivery, conduct multi-device testing, previewing on mobile phones, tablets, and TV screens to ensure no significant quality loss.
Identify scenarios unsuitable for AI workflows.
Not all brand videos are suitable for AIGC workflows. If a project demands high physical realism, such as demonstrating the operating principles of precision machinery, AI struggles to guarantee structural accuracy. If it involves specific celebrity likenesses or protected artistic styles, legal and ethical risks exist, and generative technology should be avoided. Additionally, current AI technology cannot stably reproduce scenes requiring complex long-take choreography or precise actor micro-expressions. In these cases, traditional live-action or 3D animation remain safer choices. Teams should exclude these high-risk requirements during project initiation to avoid resource misallocation. Rational assessment of technical boundaries allows AI to truly serve brand efficiency improvements rather than becoming a creative obstacle.
Execution actions involve listing a "negative list" during the requirements analysis phase, clearly marking content types prohibited from AI generation. Criteria include whether precise physical simulation is needed, whether real person likeness rights are involved, and whether complex interaction logic is required. If more than thirty percent of core shots in the project fall into these categories, we recommend abandoning pure AI solutions in favor of hybrid production modes.
Continuous iteration and team experience accumulation.
We recommend teams validate the above processes in small-scale pilot projects to accumulate proprietary style models and prompt libraries. As experience grows, gradually increase the proportion of AI application in routine marketing videos. Maintaining sensitivity to new technologies while upholding the safety baseline for brand content is key to sustainable innovation. Teams must regularly review failed cases to analyze whether issues stemmed from prompts, model limitations, or process oversights. Build an internal knowledge base, documenting successful prompt combinations, parameter settings, and pitfall avoidance guides. Through standardized operation manuals, reduce reliance on individual experience and improve overall output stability.
Specific actions include holding monthly technical sharing sessions to update model versions and plugin tools. The criterion is whether the team can directly reuse past successful templates when starting new projects, reducing trial-and-error costs by more than twenty percent. Only by making tacit knowledge explicit can true competitive barriers be built.
If you are preparing an AIGC commercial project, start by organizing the brief, reference images, product or corporate materials, delivery platforms, and copyright scope, then view theAIGC Video Services page, to translate communication from abstract preferences into executable production boundaries.
