Define the boundaries and core objectives of generative video projects
Before launching an AIGC commercial project, brands must clarify communication goals and audience profiles. Generative technology is not a panacea; it excels at handling surreal visuals, stylized transitions, or scenes difficult to achieve with high-cost live-action shooting. If the project core lies in showcasing real product details or conveying emotion through actors' micro-expressions, pure generative solutions may bring uncontrollable distortion risks. Teams must confirm during the initiation phase whether core selling points are suitable for reconstructing visual language through algorithms.
When preparing materials, provide clear brand guidelines, reference visual styles, and specific delivery platform requirements. Vague requirement descriptions lead to deviations in model training direction, resulting in large amounts of ineffective footage. Be sure to define copyright scope in the contract to ensure generated content does not infringe on third-party intellectual property, and clarify ownership of source files and final videos. This is the legal foundation for all subsequent production actions.
Use structured storyboards to curb redundancy from random generation
Traditional storyboards serve camera blocking, while AIGC storyboards must serve prompt engineering and visual consistency control. Each storyboard panel should not only describe visual content but also annotate camera movement, lighting tone, and subject form constraints. The generation process without precise storyboards is like blind men touching an elephant, easily producing large quantities of seemingly exquisite but unconnectable wasted footage.
Production teams should break down scripts into minimal visual units, setting fixed seed parameters or reference images for each unit. By limiting variables, character facial drift or scene logic breaks caused by randomness can be significantly reduced. The standard for judging storyboard quality lies in whether it can be directly translated into executable prompt structures, rather than remaining at the artistic concept level. If a storyboard cannot guide specific generation parameter adjustments, this link has serious deficiencies.
Coordinate pre-production asset preparation with prompt engineering
High-quality ControlNet images or rough 3D models are key to ensuring generation consistency. Brands need to provide high-precision 3D product models or multi-angle live-action photos as geometric constraints for the generation process. Relying solely on text descriptions makes it difficult to maintain structural accuracy for complex industrial products. Teams need to build exclusive style LoRA models or embedding vectors to solidify the brand's unique color system and material texture.
Small-batch testing must be conducted before executing generation. Verify the prompt's understanding of specific object edges, text typography, and physical laws. If severe structural distortion or logical paradoxes appear during testing, adjust control strategies immediately, such as adding depth map control or introducing pose estimation skeletons. The risk at this stage lies in over-reliance on a single model; it is recommended to adopt a multi-model cross-validation mechanism to ensure the stability of core visual elements.
Execute a hybrid workflow combining shooting and generation
For projects requiring high realism, a hybrid workflow combining live-action base plates with generative enhancement is more reliable. Live-action provides accurate lighting logic and perspective relationships, while generative technology is responsible for extending backgrounds, replacing props, or adding special effects elements. This mode can effectively avoid common physical distortion problems in pure generative videos.
During execution, cinematographers need to reserve space for generation according to the storyboard, such as using green screens or keeping backgrounds simple, to facilitate seamless compositing in post-production. Lighting setup should consider the lighting direction of generated elements to ensure unity of light and shadow at the junction of virtual and real elements. If relying entirely on generation, real camera parameters, including focal length, aperture, and shutter angle, must be strictly simulated in the virtual environment to obtain motion blur effects consistent with human visual habits.
Control temporal coherence in post-production editing
The biggest challenge in generative video lies in inter-frame flickering and discontinuous action. Editors cannot focus only on narrative rhythm; they must also assume the role of visual stabilizers. On the timeline, carefully check color matching and motion vector continuity between adjacent shots. For unavoidable flickering, use specialized deflicker plugins or optical flow interpolation techniques for repair.
Sound design plays a crucial masking role at this stage. Precise sound effects and music can divert audience attention from subtle visual flaws. Meanwhile, subtitles and graphic packaging should avoid covering areas with weak generation quality. During acceptance, focus on the stability of long takes; if obvious morphological mutations occur, consider cutting the shot or using transition effects for concealment, rather than forcing retention.
Multi-dimensional acceptance checklist and delivery standards
The acceptance process should be divided into seven independent nodes: script, storyboard, rough cut, fine cut, color grading, sound, and master. Each node requires separate sign-off confirmation to avoid repeated modifications of previously finalized content in later stages. Pay special attention to checking whether generated content contains hidden watermarks, garbled text, or physical phenomena contrary to common sense. These details often determine the brand's professional image.
Deliverables should include not only the final video but also layered project files, used prompt records, and keyframe reference images. This helps brands with secondary creation or version iteration later. If the project involves overseas distribution, additionally check the appropriateness of cultural symbols and the adaptability of multilingual subtitles. All delivered files must be packaged according to the latest platform encoding requirements to ensure playback compatibility across different terminals.
Identify scenarios unsuitable for generative technology
Not all video projects are suitable for introducing AIGC technology. When projects require extremely high news authenticity, legal evidentiary validity, or display of internal structures of precision machinery, traditional live-action shooting and 3D animation remain the only reliable choices. The hallucination characteristics of generative models make them difficult to handle scenarios requiring absolute data precision.
Furthermore, if the budget is extremely limited and the schedule is very short, the high iteration cost of generative video may become a burden instead. The time required for debugging models, screening materials, and repairing flaws often exceeds expectations. Brands need to rationally evaluate the return on investment to avoid sacrificing the clarity and persuasiveness of the content itself in pursuit of technical hype. Generative video is the optimal solution only when visual creative demands exceed existing live-action conditions and the team possesses corresponding technical control capabilities.
It is recommended to produce a short proof-of-concept video before formal large-scale investment. By running the full process practically, assess the team's mastery of tools and the stability of output quality. This step can effectively reduce trial-and-error costs for large projects, ensuring the final video both aligns with brand tonality and possesses technical feasibility.
If you are preparing an AIGC commercial project, you can first organize the brief, reference visuals, product or corporate materials, delivery platforms, and copyright scope, then view theAIGC Video Services page, to ground communication from abstract preferences to executable production boundaries.
