Define Communication Goals and Factual Boundaries
Before launching any brand content project involving AI extension, the primary task is to clarify the core selling points and non-negotiable factual bottom lines. Marketing leads must list the product's physical attributes, including material textures, structural proportions, functional interaction logic, and brand visual guidelines. These elements form the "hard constraints" of the content. If the project focuses on showcasing the product's appearance, texture, or operational details, AI must be positioned as an assistive tool while avoiding replacement solutions. At this point, the decision-making focus is to distinguish which visuals allow artistic exaggeration and which must strictly reproduce reality. Confusing the two will subject the subsequent production process to massive rework risks.
Granularity Requirements for Pre-Production Asset Preparation
High-quality AI output relies on high-fidelity input data. Production teams need to collect high-resolution product still life photos, multi-angle video footage, and detailed material texture maps. For complex curved or reflective surfaces, professional lighting setups are recommended to obtain clean Alpha channels or depth maps. In addition, the brand VI manual must be organized to ensure consistency in fonts, color codes, and graphic elements. Without these foundational assets, the AI model will have to rely on guesswork to fill in the gaps, which easily leads to product distortion or incorrect brand logos. Therefore, the completeness of the materials directly determines the credibility of the final film.
Establish a standardized asset library.
All assets uploaded to the AI platform must be uniformly named and categorized. It is recommended to build a standard angle library containing front, side, top, and close-up views. For transparent or semi-transparent materials, refractive index parameters must be provided. This standardized preprocessing can significantly reduce the probability of AI recognition errors and improve generation efficiency.
Detail locking strategy during shooting execution.
In the actual shooting phase, using live action combined with a green screen or pure black background can minimize environmental interference to the greatest extent. The photographer must use a macro lens to capture the product's minor flaws and craftsmanship marks, as these details are key to establishing realism. Meanwhile, record the on-site lighting angles and color temperature parameters to maintain consistent lighting logic during post-production compositing. For dynamic demonstration sections, it is recommended to use a stabilizer or a track to ensure smooth motion trajectories that comply with physical laws. Avoid jitter caused by handheld shooting, as AI often produces distortion artifacts when repairing unstable footage. Precise on-site control can leave ample room for correction in post-production.
Lighting consistency check.
During shooting, the intensity and position of the key light, fill light, and rim light must be recorded simultaneously. In post-production compositing, the AI-generated scene lighting must match the lighting direction of the original assets. If the shadow projection angle deviation exceeds five degrees, the reference image must be readjusted or the lighting mask must be manually drawn. This strict control over lighting logic is the cornerstone of maintaining visual realism.
Technical trade-offs and risk control in post-production.
Entering the post-production phase, the team must balance efficiency and precision. When using AI tools for background replacement or scene extension, be sure to preserve the original asset's layer structure for easy rollback at any time. For the generation or modification of the product subject, use fully automatic generation solutions cautiously, and instead adopt local inpainting technology based on reference images. This semi-automatic approach allows artists to manually adjust key frames, ensuring the product outline does not deform. Meanwhile, be alert to the over-smoothing phenomenon generated by AI, and appropriately add noise or film grain to match the texture of the live-action footage. If jagged edges or unnatural blending are found on the product edges, batch processing should be stopped immediately, and manual frame-by-frame repair should be performed instead.
Local inpainting and mask precision.
When using the local inpainting feature, the mask's feather radius must be controlled at pixel-level precision. For sharp edges such as metal bevels, a hard-edged mask should be used to ensure clear lines; for soft transition areas such as plastic shells, the feather value can be appropriately increased to achieve natural blending. After each modification, zoom in to 100% to check the details, and confirm there is no stretching or blurring before proceeding to the next step.
Sound Design and Brand Audio Identity
Beyond the visual level, the auditory experience also influences users' judgment of product quality. AI-generated sound effects are convenient but often lack the uniqueness of specific products. For example, the crisp sound of mechanical keys or the delicate sensation of liquid flow requires custom recording or a fine sampling library. During mixing, ensure a dynamic range balance among voice, music, and sound effects to avoid masking key information. If AI voiceover is used, it must undergo manual proofreading to ensure the tone matches the brand identity and has no mechanical feel. The realism of sound can compensate for minor visual deficiencies and enhance the overall immersive experience.
Acceptance Checklist and Delivery Standards
The acceptance process should be divided into multiple independent steps, targeting the script, storyboard, filming, editing, color grading, sound, subtitles, and master files respectively. Each phase requires a clear sign-off record. For AI-generated content, focus on checking whether product details match the physical object, and whether there are logical flaws or brand-violating elements. Deliverables should include the final video, project source files, and all asset packs used. If the client disputes a detail, editable project files must be provided for quick iteration. Clear delivery documentation helps reduce communication costs and ensures both parties share a consistent understanding of the project outcome.
Multi-Device Image Quality Testing
Before final delivery, playback tests must be conducted on devices with different resolutions, such as phones, tablets, computers, and TVs. Focus on observing whether AI-generated textures exhibit mosaicking or color banding on small screens. Also check subtitle readability against different backgrounds to ensure contrast meets accessible viewing standards. Only content that passes full-platform compatibility testing can be deemed a qualified deliverable.
When AI Product Video Solutions Are Not Applicable
Although AI technology is increasingly mature, it is not applicable in certain scenarios. When products have extremely high legal compliance requirements, such as medical devices or precision instruments, and no form of beautification or simplification is allowed, traditional live-action filming remains the only choice. If a brand is in a crisis PR period requiring absolutely transparent information disclosure, any uncertainty generated by AI could trigger a trust crisis. Additionally, for highly complex micro-structures or high-speed motion moments, current AI computing power struggles to guarantee physical realism; forced use will lead to severe visual distortion. In these cases, investing more resources into high-quality live-action filming is a safer business decision.
Compliance Red Line Assessment
At the initial project stage, the legal department must intervene to assess the potential legal risks of AI-generated content. Especially for visuals involving patented designs, trademark logos, and functional descriptions, AI algorithms that may produce ambiguity are strictly prohibited. If conclusive physical evidence cannot be provided to prove the authenticity of the visual content, the AI solution should be abandoned in favor of traditional studio filming to avoid potential intellectual property disputes.
Recommended next steps
We recommend brands invite a production team experienced in AIGC to participate in the evaluation during the initial project phase and jointly develop a detailed technical roadmap. Validate the feasibility of the AI workflow through pilot tests before committing to large-scale investment. Meanwhile, establish an internal review mechanism to fact-check every version of AI-generated content. Stay informed about new technologies, but always keep the brand's long-term value as the core consideration. Only by fully understanding the limitations and advantages of the technology can you make the most suitable choice for the current project.
Establish a continuous iteration mechanism
AI technology evolves rapidly. Teams should regularly review the successes and failures of past projects and build proprietary training datasets and prompt libraries. After each project, hold a retrospective meeting to summarize the successes and lessons learned in preserving details and maintaining stylistic consistency. By continuously iterating and optimizing workflows, you can gradually build the brand's own AI video production standard system, thereby gaining a competitive edge in the future.
If you are preparing an AI product video project, you can first organize the brief, reference visuals, product or company materials, delivery platforms, and copyright scope, then visitthe AIGC video services pageto move the conversation from abstract preferences to actionable production boundaries.
