Define the Boundaries and Risks of Generative Imagery During Project Initiation
Before launching an AIGC commercial project, brands must first define which shots are suitable for algorithmic generation and which require traditional cinematography. Generative technology excels at abstract concepts, surreal scenes, or large-scale repetitive elements but remains uncertain when depicting precise product structures, authentic human micro-expressions, and complex physical interactions. Marketing leads should prepare a clear list of core selling points and mark keyframes requiring high authenticity. If core selling points rely on specific material textures or precise dimensional ratios, prioritize live-action shooting or high-precision 3D modeling rather than assigning high-risk shots to purely generative workflows. Decisions made at this stage directly determine subsequent budget allocation and schedule planning; vague requirements will lead to repeated revisions or even remakes in post-production.
Compliance Review and Authorization Chain for Asset Sources
When using AIGC tools to generate video assets, the copyright status of training data and generative models must be traced. Brands should require production teams to provide proof of commercial licensing for tools used and confirm that generated content does not infringe third-party intellectual property rights. For projects combining live-action footage with generated imagery, strict asset isolation mechanisms must be established to ensure that talent likeness rights, location agreements, and music copyrights for live-action portions remain independent and clear. Review tags should specify asset source types, such as live-action, 3D rendering, AI-generated, or hybrid composites. Unauthorized open-source models or web-scraped images must not enter the commercial delivery library. If an asset source is unclear, immediately cease using that clip and replace it with proprietary assets or licensed commercial library content to avoid potential legal disputes.
Standardized Alignment of Pre-production Storyboards and Prompt Engineering
Traditional storyboard scripts must be upgraded to detailed production documents containing prompt parameters. Directors and AI artists must jointly determine style keywords, lighting logic, camera movement trajectories, and character consistency constraints for every shot. Reference images provided by the brand should serve not only as aesthetic guides but also as technical control baselines. For example, if a product must maintain a specific angle, lock perspective parameters in the prompt or use control plugins like ControlNet for geometric constraints. The risk in this phase lies in the semantic gap between textual descriptions and visual outputs; therefore, multiple rounds of sample testing must be implemented. Each iteration should record prompt versions and generation seeds to facilitate reproduction or fine-tuning. Unverified prompt strategies must not proceed directly to mass production.
Strategies for Integrating Live Action and Generation During Shooting
In AIGC commercials featuring real people or physical products, the live-action phase must leave sufficient technical headroom for post-generation. Lighting setups should avoid overly complex shadow variations to prevent flickering or artifacts when AI processes lighting consistency. Camera movements must remain steady, as excessive shaking increases tracking difficulty for generative models. Audio recording must capture clean ambient sound and dialogue separately, as current AI video generation rarely includes high-quality synchronized audio. On-set production staff must maintain detailed continuity logs, including focal length, aperture, color temperature, and object positioning; this data serves as metadata for post-production matching. If live-action footage has flaws, such as continuity errors or uneven lighting, reshoot immediately on set rather than relying on AI repair in post-production, as fixes often introduce new visual inconsistencies.
Temporal Consistency and Detail Correction in Post-Production Compositing
AI-generated videos often face issues like inter-frame flickering, object deformation, and logical discontinuities. Editors must use optical flow interpolation, stabilization plugins, and manual rotoscoping for frame-by-frame correction. Color grading must unify the color science of live-action and generated assets to ensure consistency in skin tones, product colors, and ambient lighting. Sound design must compensate for missing sound effects in generated videos by enhancing immersion through foley and mixing. Review tags at this stage must be refined to the technical level, marking shots that have undergone heavy restoration. During mid-stage reviews, brands should focus on physical plausibility during motion, such as fluid dynamics, cloth simulation, and natural character movement. Any counterintuitive physical phenomena must be corrected before the final cut; otherwise, the brand's professional image will be compromised.
Multi-dimensional Acceptance Checklist for Delivery
Acceptance checks must verify multiple dimensions based on previously agreed communication goals and platform requirements. First, check whether resolution, frame rate, and codec formats meet the latest official publishing platform specifications. Second, verify copyright documentation to ensure all asset sources are traceable. Third, conduct visual quality inspections focusing on flickering, distortion, text errors, and brand logo deformation. Finally, test playback performance across different devices to ensure color and brightness render well on both mobile and large screens. Source files, project files, and layered assets should be archived and delivered as agreed to facilitate future modifications or reuse. If major quality issues arise, such as unclear core selling points or damage to brand image, the brand reserves the right to reject acceptance and demand rectification within a specified timeframe. Projects failing acceptance may not enter the media buying workflow.
Identifying Scenarios and Exceptions Where AIGC Technology Is Unsuitable
Not all branded videos are suitable for AIGC workflows. High-fidelity product showcases, financial or medical content requiring precise legal statements, documentary-style narratives relying heavily on authentic emotional connections, and highly time-sensitive news videos are generally not recommended for generative technology. Furthermore, if a brand demands absolute control over visuals and allows zero randomness, traditional production methods should be chosen. Projects with extremely low budgets and tight deadlines may also lack cost-effectiveness due to high debugging costs. Marketing leads must rationally select technical paths based on project characteristics, avoiding blind pursuit of tech trends at the expense of content quality. When technological limitations clearly outweigh efficiency advantages, decisively revert to traditional production workflows.
Brands are advised to start with small-scale pilots in their next projects, selecting video content for non-core marketing moments to experiment with AIGC. By collaborating closely with professional production teams, gradually establish internal review standards and asset management protocols. Only after fully understanding technical boundaries and workflows should generative imagery be applied to core brand campaigns. This approach helps control risks, accumulates practical experience, and lays the foundation for future scalable application.
If you are preparing an AIGC commercial project, start by organizing your brief, reference images, product or corporate materials, delivery platforms, and copyright scope, then visit ourAIGC Video Services pageto translate abstract preferences into actionable production boundaries.
