Create, edit and star in videos with two Google Vids updates

Google Vids Updates: Two New Features That Make AI Video Creation More Accessible

Google Vids is quietly becoming one of the most practical tools for AI video creation. If you work in content, marketing, or learning and development, you have likely watched the rise of generative video with a mix of excitement and skepticism. The gap between “AI can make a video” and “AI can make our video” is wide. But two new Google Vids updates—an expanded “Create and Edit” experience and a “Star in video” feature—are helping to close that gap. Combined with custom AI visual assets from tools like Imagine Pro, these updates make it easier than ever to produce polished, on-brand videos without a full production crew.

In this article, I’ll walk through what these Google Vids updates actually mean for content teams, how they fit into a modern AI video creation workflow, and how you can pair Google Vids with an AI image generator for content creators to get results that feel far from generic.

1. Understanding the Two New Google Vids Updates

1.1 What “Create and Edit” Means for Google Vids Users

The first major update is the new “Create and Edit” experience inside Google Vids. Previously, Google Vids felt like a storyboard generator: you fed it a prompt, and it produced a rough sequence of scenes, narration, and placeholder clips. The new editing capabilities change that. You can now trim clips, rearrange scenes, layer visuals, and iterate on a generated draft without leaving the app.

That matters more than it sounds. In practice, the ability to refine an AI-generated video directly inside the tool means you can go from “a first draft” to “a nearly finished first cut” in a single sitting. Instead of exporting a rough storyboard and then rebuilding it in a traditional video editor, you can make small changes quickly. Want to remove a redundant scene? Done. Need to move a product shot earlier in the narrative? A simple drag-and-drop does the job.

For collaborative teams, this is a significant shift. Because Google Vids is built on Google Workspace, edits happen in a shared environment. Stakeholders can leave comments, suggest changes, and review versions without emailing large video files around. The approval cycle becomes shorter, and the people who understand the story can stay closer to the actual edit.

1.2 Starring in Video: How the New Google Vids Feature Works

The second update is the “Star in video” feature. The concept is straightforward: instead of setting up a studio, hiring actors, or waiting for a film crew, you can place yourself—or a teammate—into an AI-generated scene. The feature lets you create a digital representation of a person and use that representation inside the video narrative.

For many teams, this unlocks use cases that were previously impractical. A training video can feature your subject-matter expert explaining a concept while visuals change behind them. A product demo can include the founder walking through a feature even if the product doesn’t have a physical interface. A personalized onboarding message can greet a new hire with the manager’s face and voice, generated once and reused across multiple videos.

This is not a replacement for a real spokesperson. But it is a powerful option when you need to get a message out quickly and consistently. As with any AI-generated likeness, it is worth being transparent with your audience when a presenter is synthetic, especially for sensitive content like compliance training or public announcements.

1.3 Why These Google Vids Updates Matter for Content Teams

The strategic impact of these updates is difficult to overstate. Lower production costs, shorter approval cycles, and the ability to create studio-quality video with limited resources are not just nice-to-haves. They change what kind of video content a small team can produce.

Before Google Vids, producing an explainer video meant scripting, storyboarding, shooting, editing, and revising. That process could take weeks and require outside help. Now, an internal content team can draft a script, generate visuals, assemble scenes, and receive feedback in days—sometimes hours. Google Vids is positioning itself as a hub for modern AI video creation, and the new editing features make that positioning credible.

2. How Google Vids Fits into AI Video Creation Workflows

2.1 From Script to Screen: Simplifying AI Video Creation

A typical AI video creation workflow with Google Vids looks like this: you start with a script or a detailed prompt. The AI analyzes the text, suggests footage from its stock library, generates narration, and lays scenes out on a timeline. From there, you can adjust the voice, swap clips, and refine the pacing.

What makes this workflow genuinely useful is the iteration loop. The first draft is rarely perfect. But because the AI handles the mechanical parts—transcription, timeline assembly, clip matching—you can focus your energy on story and message. The tool generates, you refine.

Visual asset planning fits in after the initial script outline. Before you let Google Vids auto-select all of the visuals, identify which scenes will carry the most weight. Those are the scenes where custom imagery will have the highest impact. A generic stock clip of a conference room will not tell your brand story. A custom illustration or product-relevant image will.

2.2 Combining Google Vids with AI Visual Assets for Video

One of the biggest limitations of stock-footage-driven video tools is that the built-in library can feel recycled. You have probably seen the same handshake, the same smiling customer, and the same abstract network animation in dozens of videos. That is the visual gap that AI visual assets for video are designed to fill.

Instead of relying solely on Google Vids’ built-in clips, you can generate custom images, illustrations, and background art that match your brand and your script. Imagine a video about data security. Stock footage might show a generic server room. A custom AI visual could show a lock formed from data streams in your brand colors—an image that directly reinforces your message.

The workflow is simple: generate the custom assets, upload them into Google Vids, and place them on the timeline where they make sense. The result is a video that feels designed, not assembled.

2.3 Using an AI Image Generator for Content Creators: Fill the Visual Gap

Content creators face a constant challenge: how do you make videos stand out in a feed full of polished, AI-generated material? The answer is not better stock footage. It is more specific visuals.

This is where this AI image generator for content creators becomes a practical companion to Google Vids. Instead of hunting through dozens of stock libraries for an image that almost fits, you can generate a custom character, a product image, or a scene backdrop that is exactly what your script calls for.

For example, if you are producing a video about productivity for remote teams, you can generate a stylized image of a home office that uses your brand colors and includes your product’s visual language. That one custom image can anchor the entire video’s aesthetic, even if the rest of the footage comes from Google Vids’ built-in library. The combination of generative video structure and custom AI images is where the real creative leverage lives.

3. AI Art for Marketing: Maximizing Google Vids Output

3.1 Creating On-Brand Visuals with AI Art for Marketing

Marketing teams are often judged on visual consistency. A campaign that uses three different illustration styles or mismatched color palettes feels fragmented, even if the message is strong. That is why AI art for marketing needs to be intentional.

With Imagine Pro, you can generate images based on a consistent style prompt. If your brand uses warm earth tones and clean line art, you can apply that to every AI-generated asset. If your campaign relies on bold gradients and futuristic elements, you can keep that style across all supporting visuals.

The practical guidance here is simple: define a visual style before you start generating. Create a short style guide for your prompts—describe colors, mood, lighting, and composition. Then reuse those descriptors for every image. This is how you avoid the “random AI collage” look and build a video that feels like a cohesive campaign asset.

Let me give you a concrete scenario. A SaaS company needed to produce a series of social media ads promoting a new feature. The production budget was limited, and the timeline was tight. Instead of hiring a videographer, the marketer wrote a five-sentence script, generated a few custom product-focused images with Imagine Pro, and assembled everything in Google Vids.

The resulting ad had a custom hero image that showed the product interface in a stylized way, followed by short AI-generated video clips that communicated the core problem and solution. It looked like something produced by a larger brand. The entire production, from script to export, took one afternoon.

The same workflow applies to explainer videos and internal training. Onboarding videos no longer need a studio. A training manager can record a voiceover, generate diagrams and illustrations that match the company’s internal branding, and let Google Vids assemble the scenes. The “before” version was a dry slide deck. The “after” version is a dynamic video that people actually watch.

3.3 Measuring the Impact: Speed, Engagement, and Production Savings

It is one thing to say AI video creation saves time. It is another to measure it. Teams adopting this workflow should track three categories: production time, engagement, and cost.

Production Approach	Typical Time for a 60-Second Explainer	Approximate Cost per Video
Traditional studio shoot	2–3 weeks	$5,000–$15,000
Stock footage + human editing	3–5 days	$500–$2,000
Google Vids + custom AI visuals	2–4 hours	Minimal (software + time)

Engagement is harder to predict, but we have seen meaningful improvements when custom visuals replace stock footage. People can tell when a video was made specifically for them. A custom illustration that references your product speaks to the viewer more convincingly than a generic clip of someone using a laptop.

The savings are not just financial. They also include opportunity cost. When producing video content is fast and inexpensive, your team can test more messages, iterate on underperforming ads, and create content for smaller audience segments that would never justify a traditional production budget.

4. Best Practices for Google Vids and AI Visuals

4.1 Planning Your Google Vids Project: Script, Storyboard, and Visual Assets

Before opening Google Vids, do the planning work. Start with a one-paragraph description of the video’s objective. Then write a short script, scene by scene. For each scene, decide whether the visual should be a stock clip, a screen recording, or a custom AI image.

This pre-production step is often skipped, and the results show. AI-generated video can easily become a jumble of pretty scenes that do not support a clear message. When you identify the key scenes in advance, you know exactly where to focus your custom asset generation.

Also, decide on aspect ratios before generating any images. If your target platform is YouTube, you need 16:9. For Shorts or TikTok, you need 9:16. For social feeds, 1:1 might work. Generating images in the correct aspect ratio saves you from awkward cropping later.

4.2 Editing Tips: Pacing, Transitions, and Visual Consistency

Inside Google Vids, editing goes beyond trimming. Think about pacing. A 60-second video should move quickly, but not frantically. Use the edit mode to tighten pauses between scenes and make sure the narration has room to breathe.

Transitions matter more than you might think. If your custom AI images have a soft, painterly style, avoid cutting directly to a harsh, photorealistic stock clip. The visual mismatch is jarring. Instead, use simple cross-dissolves or match the color grading across assets. The goal is visual consistency, not maximum variety.

One specific tip: when you generate multiple AI images for the same project, try to use the same style prompt for all of them. This creates a cohesive visual language. Then let Google Vids handle the footage, and reserve custom images for the most important moments.

4.3 Common AI Video Creation Mistakes to Avoid

There are a few mistakes I see repeatedly in AI video creation workflows.

The first is inconsistent art styles. Mixing a 3D render, a watercolor illustration, and a photorealistic image in the same video feels chaotic. Pick one style and stick with it.

The second is cluttered AI-generated images. When you prompt for “highly detailed,” you can end up with an image that is so busy it distracts from the narration. Simpler compositions usually work better for video, especially when the image is only on screen for a few seconds.

The third mistake is ignoring aspect ratio requirements. If you create a beautiful 16:9 image and then try to use it in a 9:16 vertical video, you will end up cropping out important content. Always generate assets in the format you plan to publish.

The fourth mistake is relying too heavily on AI without human editing oversight. AI video creation tools are powerful, but they still produce mistakes. A word might be mispronounced, a clip might be irrelevant, or a scene might be too long. The human editor is the quality gate. Never publish AI-generated video without a full review.

5. A Closer Look at Google Vids and AI Video Creation Capabilities

5.1 Under the Hood: How Google Vids Generates Video

To get the most out of Google Vids, it helps to understand how it works under the hood. Unlike some AI tools that generate raw video frames from a text prompt, Google Vids operates more like an AI-assisted editor. It breaks your script into logical parts, matches narration to each section, suggests relevant visuals from its library, and assembles everything into an editable timeline.

The hidden insight is that prompt clarity has an outsized effect on output quality. When your script clearly describes the scene, the setting, and the tone, Google Vids makes better choices. When your prompt is vague, it defaults to generic stock footage.

Supporting visuals also strongly affect output quality. If you upload custom AI images that illustrate a specific product or story point, the AI can use those visuals to anchor the narrative. The model works best when it has strong raw material to work with.

5.2 Why Visual Asset Quality Matters for AI-Generated Video

Video is less forgiving than still images. A low-resolution image that looks fine on a phone screen can look soft and unprofessional in a full-screen video. When you are creating custom visuals for a video, resolution is not optional.

For modern video standards, you should aim for at least 1920x1080 pixels, and ideally higher if you plan to crop or zoom. A high-resolution AI image generator helps because it produces images that hold up when video editors scale, pan, or crop them. You avoid the quality loss that comes from upscaling small images.

Composition matters in video too. Leave room for the subject, because viewers may see the image alongside lower-third text or captions. And consider file format. PNG images with transparency are useful for layered scenes and logos, while high-quality JPGs work well for full-frame backgrounds. The better your source assets, the better your final video will look.

5.3 Industry Best Practices and Official Guidance

Google has consistently published guidance for Workspace users about how to approach AI-generated content: start with a clear script, use descriptive prompts, and always review the final product before sharing. Those are not just software tips; they are the basic rules of responsible AI video creation.

From a creative industry perspective, the emerging standard is to treat AI as a first-pass collaborator rather than a final authority. Use AI to generate a draft and handle repetitive tasks. Then bring human judgment to the story, the pacing, the ethics, and the brand alignment. This hybrid approach is more sustainable than trying to make the AI do everything alone.

For teams building AI video creation workflows, the best practice is to document what works. Create a prompt library for your brand, save style presets, and share successful scripts and visual styles across your team. This transforms AI video creation from a one-off experiment into a repeatable production system.

6. Getting Started with Google Vids and Imagine Pro

6.1 Setting Up a Free Imagine Pro Account for AI Visual Assets

If you want to start creating custom visuals for Google Vids, the first step is simple: create an account with Imagine Pro. The platform is designed to help marketers and content creators generate high-quality images without learning complex design tools.

After you create an account, explore the available art styles. Test a few prompts related to your brand. Save the images that work. You can then download them and upload them directly to Google Vids. There is no need to be an artist or a designer. You just need a clear idea of what you want your video to look like.

And because you can start your free trial with Imagine Pro, you can experiment without a big financial commitment. This low-risk starting point is ideal for teams that are new to AI visual assets.

6.2 Building Your First Google Vids Video with Custom AI Images

Let me walk you through a realistic first project. Open Google Vids and create a new project. Write a short script—around 150 words. Break it into three or four scenes.

Before letting the AI generate the entire video, identify one or two key scenes where you want a custom visual. Open Imagine Pro, generate an image that matches your script scene and brand style, and download it. In Google Vids, upload that image and place it on the timeline in the relevant scene.

Then, edit the rest of the footage around it. Add narration, trim any clips that do not serve the story, and make sure the custom image stays on screen long enough to be noticed. Export your video and review it from start to finish.

That workflow gives you a tangible sense of how Google Vids and Imagine Pro complement each other. You are not replacing the AI video generator. You are giving it stronger visual material to work with.

6.3 Scaling Video Production with Google Vids and AI Art Tools

Once you have built your first video, you can scale the process. The key is to create reusable assets. Build a library of custom AI images for common scenes: product shots, team illustrations, background art, and cover images. Save your style prompts in Imagine Pro so you can generate new assets that match previous work.

Over time, you will develop repeatable templates for specific video types. A product launch video might always start with a custom hero image. An internal training video might always use a consistent set of illustrations. These templates make AI video creation faster and more reliable.

Positioning Google Vids and Imagine Pro as a scalable AI video creation stack means you can produce a high volume of content without sacrificing quality. You can respond to marketing opportunities quickly, personalize videos for different audience segments, and keep your brand identity intact across every piece of content.

Conclusion

The two new Google Vids updates—richer editing and the ability to star in your own video—mark a real step forward for AI video creation. They lower the barrier to entry, shorten production cycles, and give non-editors a credible path to polished video. But the tool alone is not enough. To create videos that stand out, you need custom visual assets that reflect your brand and your story.

That is where Imagine Pro comes in. By pairing Google Vids with an AI image generator for content creators, you can replace generic stock footage with meaningful, on-brand visuals. The combination is fast, flexible, and accessible, even for small teams with limited budgets.

My advice is simple: start small. Pick one video project, write a tight script, generate a few custom images, and edit everything together in Google Vids. Once you see how quickly you can produce a compelling result, you will understand why AI video creation is not the future. It is already here, and it is more useful than you think.