Core Judgment Logic in the Initiation Phase
Before launching an AIGC commercial project, brands must prioritize aligning communication goals with core selling points. Generative imagery excels at constructing surreal scenes or abstract concepts but lacks controllability when displaying physical details of tangible products. If the core requirement is to precisely present product materials, interface specifications, or handling feel, pure AIGC solutions often fall short. In such cases, evaluate whether to adopt a hybrid workflow combining live-action with partial generation. Teams must confirm the audience's tolerance threshold for visual realism; social media short-video users typically accept stylized content more readily than traditional TVC viewers. Defining delivery platforms is also critical, as different channels have vastly different requirements for resolution, aspect ratio, and encoding formats, which directly determine the precision standards for early-stage asset generation.
Completeness Requirements for Pre-production Materials
Production teams need structured basic materials from brands, not scattered inspiration images. The core material package should include high-definition 3D product models or multi-angle live-action reference images, brand visual guideline manuals, and clear script storyboards. For scenes involving human interaction, provide actor image licenses or specify digital human settings. Reference images are only for establishing artistic tone and cannot serve as legal basis for final delivery. Brands must declare copyright scope in advance, clarifying whether third-party IP elements are allowed in generated content. If using open-source models to train exclusive styles, confirm the compliance of training data. Lack of clear storyboards leads to endless trial-and-error in generation, significantly increasing time and computing costs. All provided materials must undergo internal review to ensure no intellectual property disputes, clearing obstacles for subsequent commercial use.
Staffing and Collaboration Mechanisms
AIGC video production breaks traditional film industry role boundaries, requiring cross-disciplinary collaboration. Core roles include prompt engineers, 3D asset designers, compositors, and directors familiar with AI tool workflows. Prompt engineers translate creativity into semantic instructions understandable by machines, requiring strong language decomposition skills. 3D asset designers build basic geometric structures and lighting environments, providing spatial constraints for generation models. Compositors focus on fixing generation artifacts and unifying visual style. All parties must establish a unified glossary and file naming convention at the project start to ensure communication efficiency. Hold regular progress reviews to calibrate generation direction and avoid deviating from the creative track. Project managers must coordinate deliverables at each node, ensuring smooth information flow and reducing rework caused by misunderstandings.
Execution Standards for Shooting and Material Capture
In hybrid workflows, live-action provides geometric constraints and lighting benchmarks for AI generation. Photographers must use high-dynamic-range equipment to capture key product features, ensuring texture details are preserved in post-compositing. Lighting setups should avoid overly complex reflections to prevent AI from producing logically incorrect artifacts during repainting. If relying entirely on generative imagery, precisely describe camera movement trajectories, focal length changes, and depth-of-field effects via text prompts. Here, virtual camera parameter settings equate to camera selection in traditional shooting. Teams must record seed values and parameter combinations for each generation to maintain visual consistency during revisions. Any uncontrolled randomness may cause discontinuities between shots, breaking narrative coherence. Retain original RAW format data during capture to leave maximum room for post-production adjustments.
Controllability Management in Post-production
The focus of post-production is integrating generated materials into a unified visual system. Editors must establish strict version control mechanisms, distinguishing between raw generated files, intermediate composite layers, and final masters. Color grading is not just for aesthetics but to unify color temperature and contrast across materials from different sources, eliminatingpin jie traces. Sound design must closely match visual rhythm to compensate for missing physical feedback in generated visuals. Subtitles and graphic packaging should comply with brand guidelines, avoiding obstruction of key product information. If using AI for lip-syncing or voice generation, manually proofread emotional tone and naturalness to prevent mechanical feelings from weakening brand affinity. All added effects must serve functional explanation, avoiding showmanship that interferes with information delivery. Compositors must check edge blending frame by frame to ensure smooth and natural visual transitions.
Key Checkpoints for Final Acceptance
The acceptance phase requires item-by-item verification against pre-approved storyboards and scripts. First, check if product appearance matches the physical item, focusing on logo shape, button layout, and color deviation. Second, assess visual logic, confirming that lighting direction, perspective relationships, and physical movements conform to common sense. For audio, check volume balance, background noise control, and accuracy of multilingual subtitles. Technically, verify that file format, bitrate, and resolution meet publishing platform requirements. Brands should retain source files and project documentation for future tweaks or secondary creation. If major logical errors or copyright-disputed content are found, propose revisions within the contractually agreed scope to avoid infinite rework. Acceptance criteria should be confirmed in writing at project initiation as the sole basis for final delivery.
Risk Control and Boundaries of Applicability
Not all video projects are suitable for generative technology. When projects require strictly legally backed proof of authenticity, such as medical device operation demonstrations or financial data disclosure, pure generated visuals may trigger compliance risks. If budgets are extremely limited and schedules tight, AIGC debugging costs may exceed traditional shooting. For brand stories emphasizing real human emotional connection, digital humans or generated backgrounds often lack subtle expressive tension, failing to resonate with core audiences. Additionally, if products are in early prototype stages with frequent appearance changes, low reuse rates of generated assets lead to poor ROI. Teams must honestly assess their technical reserves to avoid project delays or quality loss due to blind pursuit of new technology. For topics involving sensitive political, religious, or violent content,jian jue avoid using generative tools to prevent uncontrollable ethical controversies.
Next Step Recommendations
Brands are advised to start with small-scale test projects to verify their internal teams' or suppliers' mastery of AIGC workflows. Audit existing video assets to identify which links can improve efficiency through generative technology. Communicate deeply with production partners to clarify responsibility boundaries regarding copyright, data security, and delivery standards. Monitor the latest industry tech trends but remain rational, avoiding direct application of experimental technologies to core marketing campaigns. Through gradual iteration, establish AI video production standards suited to your brand tone, achieving steady integration of technology and creativity. Regularly review pain points in project execution, optimize collaboration processes, and form replicable methods to lay the foundation for future large-scale application.
If you are preparing an AIGC commercial project, first organize the brief, reference images, product or corporate materials, delivery platforms, and copyright scope, then viewthe AIGC video service pageto ground communication from abstract preferences to executable production boundaries.
