Core Asset Inventory During Project Initiation
Before launching an AIGC commercial project, brands must complete a digital inventory of core assets. This is not just material collection but establishing a baseline for generative workflows. Teams must confirm the resolution, frame rate, and lighting consistency of existing live-action footage. If original materials have severe noise or motion blur, subsequent AI restoration costs will increase significantly. Decision-makers should define communication goals and audience profiles to avoid blindly introducing technical tools without clear selling points. Reference visuals should provide static images or short video clips with clear styles, rather than relying solely on text descriptions. Copyright scope must be defined before contract signing to ensure undisputed commercial usage rights for training data and generated content. If high-quality drafts are unavailable, the project may fall into an inefficient cycle of repeated redrawing.
Managing Controllable Variables During Shooting Execution
Traditional shooting emphasizes perfect imaging in one take, whereas workflows combining AIGC shift focus to providing highly editable intermediate materials. Photographers should retain more negative space to facilitate frame expansion via generative fill in post-production. Lighting setups should avoid extreme contrast to prevent artifacts when AI processes shadow details. Actor performances must maintain action continuity to reduce the risk of temporal discontinuity from rapid editing. Although audio can be replaced later, on-site ambient sound helps maintain natural lip-sync synchronization. Production teams must record parameters for each shot, including focal length, aperture, and color temperature, as these data are key to maintaining visual consistency in post-production. If sufficient green screen or tracking points are not reserved on set, post-production compositing difficulty will rise exponentially.
Staffing and Collaboration Process Optimization
AIGC video production requires teams to possess interdisciplinary capabilities. Directors must understand how prompt engineering affects image generation, accurately translating creativity into machine-readable language instructions. Art directors must shift from single-frame aesthetics to controlling consistency across dynamic sequences, ensuring materials generated in different batches unify in tone and texture. Post-production VFX artists must master layer separation and mask drawing techniques to precisely control AI-generated region boundaries. Project managers must establish real-time feedback mechanisms to shorten the turnaround cycle from generation results to revision comments. Team members must share a unified asset library and style guide to avoid fragmented outputs caused by individual interpretation biases. Regular technical review meetings should summarize the characteristics and limitations of commonly used models, forming an internal knowledge base to improve overall efficiency.
Versioning Strategy in Post-Production Workflows
Entering the post-production phase, teams must establish strict version control mechanisms. While AIGC tools excel at generating variants, a lack of logical constraints can easily lead to visual drift. Editors should first lock the main narrative rhythm before applying generative optimization to specific shots. Color grading must unify LUT presets before generation to prevent color discrepancies among materials generated in different batches. Sound design must align strictly with visual dynamics to avoid mismatches between AI-generated lip shapes and audio. Subtitle creation must consider safe display areas across multiple platforms to ensure key information is not obscured by interface elements. Before mastering output, check the edge blending of all generated regions to eliminate obvious algorithmic traces. Without a clear folder structure, multiple rounds of revisions can easily cause file confusion, leading to delivery delays.
Adaptation Logic for Multi-Platform Distribution
Different social platforms have varying requirements for video aspect ratios, duration, and encoding formats. Teams must derive vertical, square, and horizontal specifications from the master version. While AIGC technology excels at recomposing frames, manual intervention is needed to ensure the subject remains prominent after cropping. Short versions should distill core selling points, while long versions focus on brand story completeness. Overseas marketing videos must ensure cultural symbol compatibility to avoid misunderstandings from generated content. Social media short videos emphasize visual impact in the first three seconds, achievable through AI-enhanced dynamic effects. Before delivery, check technical parameters against the latest official guidelines for each platform, as frequent rule updates may cause upload failures or quality compression if relying on outdated experience. Failure to optimize metadata for different platforms will directly affect content exposure rates.
Key Checklist for Acceptance Phase
Acceptance should not focus solely on the final film's look but break down into script, storyboard, editing, color grading, sound, and source files. Brands must verify whether generated content deviates from established brand guidelines, such as logo distortion or colorshi zhen. Check the physical logic rationality of AI-generated parts to avoid common-sense errors like anti-gravity or limb distortion. Confirm complete commercial licensing chains for all music, fonts, and materials to mitigate legal risks. Source file delivery must include project files and layered assets for future secondary modifications. If major logical loopholes are found during acceptance, trace back to the early storyboard stage to identify causes rather than merely patching in post-production. Do not start large-scale distribution before signing a written acceptance confirmation to avoid liability disputes due to unclear revision requests.
Risk Warnings for Inapplicable Scenarios
AIGC is not a universal solution; traditional production methods are more reliable in certain scenarios. For documentary-style content heavily reliant on genuine emotional interaction, AI struggles to simulate subtle human facial expressions. Videos involving precise product structure displays may suffer from geometric distortion in generative images, misleading consumers. For medical or financial promotions requiring strict legal compliance, AI's unexplainability may bring regulatory risks. Projects with extremely limited budgets and tight deadlines may incur higher trial-and-error costs if attempting new technologies without mature workflow support. If brands cannot provide clear requirement documents, AI tools will fail to perform effectively, often yielding results below expectations. Under these boundary conditions, sticking to live-action shooting or traditional CGI may be more economical choices.
Next Step Action Recommendations
Brands are advised to test the compatibility of AIGC workflows with existing teams on a small scale before launching projects. Organize past high-quality cases as style references and define non-negotiable brand red lines. Collaborate with production teams to develop detailed milestone plans, incorporating generative steps into critical path management. Monitor the latest industry technical trends but avoid blindly chasing new tools unverified by commercial use. Accumulate experience through small-scale pilot projects to gradually establish AI video production standards suitable for your brand. ONCE's official website publicly covers services for corporate promotional videos, brand promotional videos, TVC commercials, product videos, overseas marketing videos, social media short videos, and AIGC video production; consult specific service scopes based on actual needs.
If you are preparing an AIGC commercial project, first organize the brief, reference visuals, product or corporate materials, delivery platforms, and copyright scope, then viewthe AIGC Video Services pageto ground communication from abstract preferences to executable production boundaries.
