Translating Brand Visuals During Project Initiation
Before launching an AIGC ad project, brands must translate abstract concepts into concrete instructions. Traditional filming relies on directors’ secondary creation of scripts, whereas generative imagery requires converting color systems, font specifications, and composition preferences from brand manuals into prompt structures understandable by models. Marketing leads should compile high-performing visual assets, including HD product images, standard color codes, and typical scene references. Without clear visual anchors, generated results easily deviate from brand tone. Teams must confirm whether core selling points are suitable for algorithmic reconstruction rather than relying solely on live-action texture. The key action at this stage is building a visual keyword library, distinguishing mandatory brand elements from flexible background details. The risk lies in over-relying on general models, leading to homogeneous visuals; therefore, compatibility with specific style LoRAs or fine-tuned models must be tested in advance.
Structured Organization of Reference Assets
High-quality ControlNet input depends on structured reference assets. Images provided by brands should not just be marketing posters but include multi-angle product shots on white backgrounds, material close-ups, and scene photos with clear lighting logic. Production teams must preprocess these assets to extract edge detection maps, depth maps, and normal maps to lock product forms during generation. For products with complex curves or transparent materials, additional specifications for refractive and reflective indices are required. Exceptions involve dynamic function demonstrations, where static references are insufficient; simple 3D rough models or live-action green screen footage are needed to guide motion trajectories. Unverified reference images cause geometric distortions in generated frames, increasing post-production repair costs. The consequence is repeated prompt modifications failing to converge on frame structure, delaying overall progress.
Staffing and Collaboration Mechanisms
AIGC video production requires close coordination across cross-functional teams. The core team should include prompt engineers, visual designers, and compositors. Prompt engineers translate creativity into machine language, visual designers uphold aesthetic standards, and compositors resolve technical flaws. Brands must designate a single point of contact to avoid conflicting instructions from multiple stakeholders. We recommend adopting an agile development model with daily iterative cycles. Daily stand-ups should sync generation progress and technical bottlenecks to ensure issues are resolved within the day. Team collaboration efficiency is measured by the speed of the feedback loop, i.e., the time from submitting revision requests to viewing new versions. Prolonged communication chains severely slow down project pace.
Managing Controllability in the Generation Process
The core challenge in AIGC ad production is balancing randomness with consistency. Production teams should adopt a layered generation strategy, handling backgrounds, subjects, and effects separately. Use ControlNet technology to fix product contours and regional prompts to precisely describe local lighting and materials. Brands must review intermediate drafts, focusing on the integrity of brand logos and the accuracy of product details. The criterion is whether keyframes match storyboard presets, not the artistic quality of individual frames. The risk lies in model misinterpretation of complex instructions, potentially causing text garbling or logo distortion. In such cases, introduce post-production retouching for correction rather than attempting to solve all issues through infinite redraws. For overseas marketing videos, ensure cultural symbol adaptability to avoid generating visually ambiguous elements.
Hybrid Workflow Combining Live Action and Generation
Purely generated imagery cannot fully replace the physical realism of live action, making hybrid workflows the mainstream choice. Teams can conduct small-scale live shoots to capture actor performances, real lighting, and environmental interaction data, using them as the basis for generation. This approach retains the emotional connection brands need while leveraging AI’s advantages in scene expansion and effect enhancement. During shooting, reserve sufficient space for post-production compositing, such as using HDR recording and solid-color backgrounds. In editing, ensure consistency in frame rate, resolution, and color space between live-action and generated assets. Significant discrepancies cause visual fragmentation. This approach suits projects with limited budgets seeking high visual impact or social media short videos requiring rapid iteration of visual styles. It is unsuitable for industrial demo videos demanding high precision in physical interactions.
Detail Calibration in Post-Production Compositing
Generated assets often contain flickering, artifacts, or logical errors, requiring frame-by-frame calibration via professional post-production software. Color grading must strictly adhere to brand standard colors to ensure consistency across different generation batches. Sound design should closely align with visual rhythm to compensate for auditory deficiencies in generated imagery. Subtitles must follow platform guidelines, paying attention to font licensing and readability. Teams must establish version control mechanisms to record parameters and effects of each modification for tracing issue sources. Acceptance checks should focus on motion fluidity to avoid visual fatigue caused by inter-frame inconsistencies. The risk is that excessive polishing masks the limitations of generation technology, resulting in unnatural final outputs. This may negatively impact the construction of a premium brand image, especially in long-cycle placements like TV commercials.
Developing a Multi-Dimensional Acceptance Checklist
The acceptance phase should cover script, storyboard, editing, color grading, sound, and master delivery. Brands must evaluate whether the final video effectively communicates core selling points based on previously confirmed communication goals and audience profiles. Check items include logo clarity, product function accuracy, and emotional resonance strength. For AI product videos, verify the accessibility and scientific accuracy of technical principle demonstrations. Clearly define copyright scope to ensure undisputed commercial usage rights for generated assets. Technical requirements for delivery platforms should follow the latest official standards prior to release to avoid quality compression due to format mismatches. If major deviations are found, activate contingency plans, such as replacing certain shots or adjusting narrative pacing. Failed acceptance items must be documented in writing, specifying responsible parties and deadlines.
Applicability Boundaries and Next Steps
AIGC ads are not a universal solution; their applicability depends on project type and resource constraints. For projects emphasizing real experiences, complex physical interactions, or high-precision product displays, traditional live action remains irreplaceable. Generative imagery is better suited for conceptual promotion, stylized expression, and rapid content mass production. Brands must balance creative freedom with cost control when making decisions, avoiding blind pursuit of tech trends. We recommend starting with small-scale social media short videos to accumulate data feedback and production experience before expanding to corporate promos or TV commercials. ONCE provides AIGC video production and related workflow services to help teams build efficient generative imagery production systems. Future collaborations require customized solutions based on specific needs to ensure deep integration of technology and brand.
If you are preparing an AIGC ad project, first organize your brief, reference images, product or company materials, delivery platforms, and copyright scope, then view theAIGC Video Services pageto ground communication from abstract preferences to executable production boundaries.
