How to Define the Decision Weight Between AI Generation and Live Action During Project Initiation
When launching an AIGC commercial project, the primary task is to classify shot attributes based on the security of brand assets and the certainty of communication goals. Marketing directors must establish a binary evaluation matrix to categorize every shot in the script as either a core trust anchor or an atmospheric support element. Core trust anchors typically include product appearance details, key feature demonstrations, brand logos, and spokesperson close-ups; these shots directly impact consumer purchasing decisions and legal compliance, and must prioritize live action or use authorized official 3D assets as the generative base. Atmospheric support elements include background environments, abstract concept visualization, transition effects, and emotional rendering of non-specific characters, which are better suited for AI generation to control costs and expand visual imagination.
A risk anticipation mechanism must be introduced during the decision-making process. If a brand is in the launch phase of a new product and its appearance has not yet been disclosed, it is strictly prohibited to use pure text-to-video models directly for product shots, as model hallucinations may cause the product structure to deviate from the physical item, sparking false advertising disputes. In such cases, a workflow combining live-action footage with AI post-production repainting should be adopted to ensure physical authenticity. Conversely, if the project aims to convey brand philosophy or future vision without a specific physical counterpart, the proportion of AI generation can be significantly increased. Project initiation documents should clearly mark the technical implementation path and corresponding acceptance criteria for each shot to avoid rework caused by cognitive misalignment during mid-production.
Baseline Materials and Rights Confirmation Documents Required from the Brand
AIGC workflows have far higher requirements for the completeness of input materials than traditional production. Brands cannot simply provide a text script or verbal brief; they must prepare a structured visual baseline package. This package should include high-precision, multi-angle product photography or CAD model files to train or constrain the AI model's output; standardized brand color values, typography guidelines, and vector logo files to prevent the AI from altering the brand's visual identity system during generation; and previously approved official visual assets to serve as reference images for style transfer. Without these baseline materials, AI-generated visuals will default to generic stock footage styles and fail to carry the brand's unique asset value.
Copyright and likeness authorization are other prerequisites that cannot be overlooked. Before using AI to generate imagery involving human likenesses, the brand must confirm whether it holds full commercial usage and AI adaptation rights for those likenesses. If live-action actors are filmed and their footage is later stylized by AI, the contract must explicitly define the ownership of the AI-processed footage and the scope of derivative works. Commercial licensing checks must also be completed for background music, fonts, and reference images for specific artistic styles. For projects with missing materials or unclear authorizations, the production team should issue a written risk warning during the project approval review and proceed only after the legal or compliance department issues a feasibility confirmation report.
Execution Milestones and Quality Control in Hybrid Production Workflows
The focus of AIGC commercial production is the iterative loop between live-action shooting and AI generation. During the live-action shoot, the director of photography must adapt traditional lighting logic to accommodate post-production AI processing. For example, if AI background replacement is planned, on-set lighting should avoid overly complex environmental reflections while retaining sufficient highlight and shadow information for later separation; if AI face replacement or stylization is planned for the talent, actors must keep facial muscle movements natural and avoid extreme expressions that could distort the generated results. Script supervisors must meticulously record the focal length, aperture, color temperature, and camera movement parameters for every shot, as this data provides the key constraints for maintaining consistency in subsequent AI generation.
In the AI generation and post-production compositing phases, the core of quality control lies in consistency management. The production team should build dedicated style models or LoRA libraries rather than generating from new prompts each time, ensuring unified characters, lighting, and art styles across different shots. Internal review checkpoints must be set for keyframe generation results, requiring brand approval before batch extension. If perspective, proportion, or color discrepancies are found between the AI-generated content and live-action footage, the team should immediately revert to adjust prompts, ControlNet parameters, or reshoot partial footage, rather than attempting to force integration solely through color grading. This hybrid workflow requires the production team to have cross-disciplinary coordination skills to synchronize progress and feedback between the live-action and AI teams in real time.
Phase-Specific Acceptance Criteria and Deliverables Checklist
Acceptance of brand AI videos cannot rely solely on the final cut's visual appeal; it must be broken down into verifiable, independent phases. In the script and storyboard phase, the focus is on verifying the accuracy of AI feasibility annotations and identifying descriptions that are technically unfeasible or legally high-risk. During the shooting phase, the acceptance focus shifts to footage quality, checking whether the live-action shots leave sufficient room for AI processing and whether metadata records are complete. In the initial AI generation phase, product fidelity, brand element accuracy, and motion continuity must be verified frame by frame; since revision costs are lowest at this stage, it should receive the most review effort.
In the post-production compositing and color grading phases, acceptance criteria return to the fundamentals of commercial video, checking whether editing pace, sound design, subtitle layout, and color grading comply with broadcasting platform standards. Upon final delivery, in addition to the master file, layered project files, original AI-generated frame sequences, and records of the model versions used must also be provided. These source files serve not only as archives for the current project but also as the foundation for reusing brand digital assets in the future. If process files are missing from the deliverables, the brand will be unable to update content cost-effectively for future marketing campaigns, resulting in having to start from scratch for every deployment. The acceptance form should be signed by the brand, the production agency, and the legal department to clearly define the boundaries of responsibility for each phase.
Specific Acceptance Points for Audio and Subtitles
AI-generated videos often suffer from audio-visual desynchronization or robotic voiceovers, requiring separate acceptance criteria. If the voiceover is AI-generated, manual proofreading of pronunciation, stress, emotional intonation, and technical terminology accuracy is mandatory to avoid flat narration that contradicts brand tone. Sound effects and background music must be checked for copyright status and verified for dynamic alignment with AI-generated visuals to prevent audio-visual misalignment caused by unstable frame rates. Subtitle acceptance requires not only verifying text content but also checking readability against complex AI-generated backgrounds, adding outlines or masks when necessary. Although audio and subtitles are post-production elements, their quality directly impacts audience acceptance of AI videos, and traditional production standards must not be lowered simply because the technology is novel.
Applicable Boundaries and Negative List for AIGC Commercials
Although AIGC technology expands creative possibilities, not all brand video projects are suited for this workflow. The following scenarios warrant caution or should avoid using AI generation as the primary production method. First, for industrial products or medical devices that highly depend on physical authenticity, if the internal structure, material texture, or operational logic allows zero margin for error, live-action combined with traditional CGI remains the safer choice. Second, for content involving sensitive social issues, political figures, or minors, the unpredictability of AI generation may trigger public relations risks or ethical controversies. Third, for projects with extremely low budgets and tight deadlines, while AIGC appears cost-effective, a lack of sufficient time to fine-tune models and review outputs may instead lead to budget overruns and delays due to repeated revisions.
Furthermore, brands must clearly recognize the current limitations of the technology. AI-generated video still falls short in long-take continuity, multi-person interaction logic, and precise text rendering; if a script heavily relies on these elements, alternative solutions should be planned in advance. For brands pursuing an ultra-realistic style whose target audiences are highly sensitive to AI artifacts, overusing generative technology may damage brand trust. Before project approval, it is advisable to produce small-scale test clips to validate the technical approach and audience reception rather than going all in immediately. Defining inapplicable conditions ensures safer project execution more effectively than blindly chasing technology trends.
Extract Methodologies from Case Studies Rather Than Copying Formats
Outstanding industry cases are often used as references for project approval, but brands must be wary of equating a case's visual format with the elements of success. The value of studying cases lies in understanding how they solved specific narrative challenges, such as balancing epic scale with production feasibility through a mix of real and virtual elements, rather than copying their shot language or technical parameters. Since each brand's audience perception, product characteristics, and distribution channels differ, directly replicating another's format often leads to poor market fit. The correct approach is to extract the decision-making logic from the case, such as why they chose live-action in one scene and generative AI in another, and then re-evaluate based on your own conditions.
During internal reporting or vendor pitches, avoid using only case screenshots as the basis for requirements. Instead, provide an analyzed adaptability statement indicating which experiences can be transferred, which need adjustment, and which are entirely inapplicable. Production teams should also proactively guide clients beyond superficial imitation, focusing on how a hybrid workflow can solve the brand's specific communication challenges. Case studies are tools to inspire thinking, not blueprints for execution. Only by internalizing external experience into judgment criteria that fit actual conditions can AIGC commercials truly serve long-term brand building rather than becoming a one-off technology showcase.
Next Steps and Resource Preparation
If you are evaluating the feasibility of AIGC commercials or AI product videos, it is recommended to start by auditing your existing brand digital assets. Inventory available high-resolution product images, 3D models, archival footage, and licensing documents to assess whether they meet the input requirements for an AI workflow. Next, select a low-risk, small-scale communication need as a pilot project to validate the actual performance of the hybrid production process regarding your company's internal approvals, legal compliance, and audience feedback. The pilot project does not need to be perfect; the focus is on establishing collaboration mechanisms and acceptance standards.
The ONCE website publicly offers services covering corporate videos, brand videos, TV commercials, product videos, overseas marketing videos, social media short videos, and AIGC video production, supporting full-process collaboration from project consultation to final delivery. We recommend that brands communicate deeply with production teams possessing practical AIGC experience before formal project approval to clarify technical boundaries and expected outcomes. The more thorough the preparation, the lower the uncertainty in subsequent execution. Please adjust delivery specifications according to the latest official requirements of the publishing platforms to ensure compliant content launch.
If you are preparing an AIGC commercial project, you can first organize the brief, reference visuals, product or company materials, delivery platforms, and copyright scope, then review theAIGC Video Services pageto translate abstract preferences into actionable production boundaries.
