Topview

1. AI Video Agent
   Enter a product link or upload product images, and it automatically extracts the selling points, writes a script, matches suitable shots, and produces a short video.
2. Product-Holding Digital Avatars
   Generates digital avatars that can hold, display, and explain products for talking-head videos, product demonstrations, and advertising assets.
3. URL-to-Video
   Reads images and text from product pages on Amazon, Shopify, Taobao, and other platforms, then automatically creates corresponding marketing videos.
4. Reference Video Recreation
   Analyzes the pacing, shots, and structure of an existing video, then replaces its product with your own to generate a similarly styled version.
5. Multilingual Scripts and Voiceovers
   Generates multilingual copy and voiceovers for different markets, reducing the work required to recreate cross-border marketing assets.
6. Canvas Storyboarding
   Uses storyboards, text, and shot descriptions to control visual content, actions, and camera movement, reducing unusable results caused by entirely random generation.

I first tested it with a product image of a pair of Bluetooth earbuds.

After uploading the image, I asked it to create a product introduction video of around 15 seconds.

The script and digital avatar were generated quickly.

The most interesting part was not the spoken presentation, but the fact that the avatar actually picked up and displayed the earbuds.

The product was not simply pasted into the person’s hand. There was a clear interaction between the hand and the item.

That makes it more suitable for selling products than an ordinary AI Avatar.

However, the interaction does not look natural every time.

At complex angles, problems can still appear around the fingers and the edges of the product.

The smaller and more detailed the item is, the more likely the AI-generated artifacts are to become visible.

A first result that looks acceptable is therefore not necessarily ready to use in an ad campaign.

One-click generation is better for quickly testing a direction.

If you simply want to see what a particular selling point might look like as a video, direct generation is the fastest option.

When preparing assets for an actual campaign, I would rather make adjustments in Canvas.

For example, the first shot could show the packaging, the second could show the person picking up the product, and the third could cut to a close-up.

Breaking the requirements into separate shots produces a more reliable visual sequence than allowing the AI to decide the entire video on its own.

The same applies to lip-syncing and facial expressions.

Quick generations may occasionally have mouth movements that fall behind the audio or expressions that look stiff.

That is sufficient for evaluating the structure of an ad, but it is still some distance from authentic human footage.

Topview is better suited to testing many variations than producing one premium advertisement.

A traditional shoot may take a great deal of time and produce only three to five usable assets.

With Topview, it is easier to generate more than a dozen versions at once, change the opening, script, or Avatar, and keep the variations that perform best.

For TikTok, Reels, and performance advertising, this approach is more aligned with how campaigns actually work.

A large proportion of advertising assets will be discarded anyway.

It is not unusual for only two or three out of ten versions to produce meaningful results.

If production costs are low enough, creating more variations for testing becomes more important.

The limitations of the free version are also fairly obvious.

It includes a watermark and cannot be used directly for commercial purposes.

Its main purpose is to help you determine:

Whether the product-holding avatar looks acceptable, whether the script is moving in the right direction, and whether the overall workflow is more efficient than your current production process.

For actual batch ad production, you still need to calculate the credit costs of the Pro or Business plan.

Pros and Cons

Pros

• It has a clear e-commerce focus. Instead of starting from a blank Prompt, it creates videos around the product and its selling points.
• Product-holding avatars are distinctive. They can hold products and display details, making them more suitable for selling than standard talking-head avatars.
• It can use product links directly. You do not need to copy and reorganize the selling points from a product page manually.
• Multiple models are available on one platform. There is no need to subscribe to and switch between separate generation websites.
• Canvas provides more control. You can break down and adjust individual shots before launching a campaign.
• It suits high-frequency creative testing. You can quickly change the script, presenter, and video direction for the same product.

Cons

• The free version cannot be used directly for commercial purposes. Its watermark and licensing restrictions make it more of a trial option.
• The avatars can still look AI-generated. Facial expressions, lip-syncing, hands, and product interactions may occasionally appear unnatural.
• One-click generation has limited consistency. Improving the success rate usually requires adjusting the storyboard and Prompt.
• Video duration is relatively short. Pro supports around 15 seconds and Business around 25 seconds, making them better suited to ad clips than long-form content.
• Credit costs are not fixed. The model, duration, and number of regeneration attempts all affect the actual cost per video.

Who It Is and Isn’t For

Best for:

• E-commerce operators and performance marketers. They need a constant supply of new assets for A/B testing and ROI optimization.
• Cross-border sellers. They need to create multilingual product videos quickly for different markets.
• Independent-store and TikTok Shop sellers. They lack a permanent production team but require a large volume of short videos.
• Dropshipping sellers. Their products change too quickly to arrange a new human shoot for every item.
• Marketing agencies. They manage multiple clients and products and need to reduce production time per asset.

Not ideal for:

• People seeking premium, brand-level advertising. AI avatars and generated shots cannot yet replace professional production consistently.
• People who need long-form videos. Current plans focus more on advertising assets lasting from a few seconds to a few dozen seconds.
• People who only intend to create one or two videos. Without a need for batch production, the platform’s advantages are less significant.
• Sellers with no budget at all. The free version cannot be used directly for commercial advertising.
• People who expect every first generation to be usable. AI video still requires selection and regeneration.

Topview AI is not best suited to “making a cinematic masterpiece for you.”

Its real value is making a traditionally demanding ad-creative production process lighter.

Provide a product image.

Have a digital avatar hold it.

Try a different opening.

Replace the copy.

Generate several variations at once, then put them directly into testing.

For performance advertising, this workflow is more meaningful than creating one exceptionally polished video.

However, you need to account for unusable generations.

If producing one usable 15-second ad requires five or six attempts, credit consumption will increase very quickly.

For your first test, take one product you are currently selling and run it through the free version.

Do not focus only on whether the Avatar looks good.

Check whether the script captures the product’s selling points, whether the interaction with the product looks natural, and how many revisions it takes to go from uploading the product to obtaining the first usable video.

If this workflow is clearly faster than hiring creators, filming, and editing, then use Pro to produce a batch of real campaign assets.

Finally, evaluate the ROI.

Whether Topview is worth paying for does not depend on how many ads it can generate in one minute.

What matters is this: with the same creative budget, can it help you test more variations that produce meaningful performance data?