Decide what kind of “video” you need
“AI video” covers a lot of ground. Some tools turn one sentence into a few seconds of cinematic footage. Others put a virtual presenter in front of the camera to explain a product. Others work on footage you already have: keying, extending frames, restyling. Different goals lead to very different choices.
Most needs fit into four directions: text-to-video, image-to-video, editing and effects, and digital-human presenters. Many platforms cover several, but each usually has only one or two real strengths.
Text-to-video: good for creative exploration
Text-to-video gets the most attention. You type a description and the model returns a short clip. Well-known options include Kling AI from Kuaishou, ByteDance’s Jimeng AI and Seedance, Vidu from Shengshu Technology, MiniMax’s Hailuo, and the early Western entrants Runway and Pika.
These tools suit concept films, first drafts of ad ideas, and B-roll or transitions in short-form video. Their shared limits are just as clear: clips are short, complex motion and multi-person interaction often break, and a character may look different from shot to shot. If your project needs one character throughout, put “character consistency” or reference-image features at the top of your test list.
Give several tools the same prompt a few times each and compare three things: does the image match the description, does the motion look natural, and how stable are the results across runs? Curated showcase reels tell you little about everyday quality.
Image-to-video: more control
If you have a precise picture in mind, design key frames with an image tool first and then let a video model animate them. That is usually more controllable than pure text. Most major platforms support image-to-video, and some support first-and-last-frame control, where you set the opening and closing frames and the model fills in the motion between them.
This workflow fits e-commerce product shots, animated illustrations and storyboard checks. The source image’s quality and composition directly shape the result. Hands, text and fine structures tend to warp in motion, so it helps to keep requested movement modest.
Editing and effects: working with existing footage
Not every video has to be generated from scratch. Runway was early to offer green-screen removal, frame extension and style transfer, and Pika can make local edits to existing clips. For teams that already shoot footage, these features can replace part of post-production.
Judge editing tools on different criteria: clean matte edges, no flicker after processing, high-resolution export, and smooth hand-off to your existing editing software.
Digital humans: the workhorse for explainers, training and marketing
Digital humans are among the fastest areas to reach commercial use. HeyGen, Chanjing, Youyan and Jimeng AI all generate avatar videos: pick or build an avatar, paste a script, and get a lip-synced presenter.
Typical uses are product introductions, course content, internal training and multilingual marketing. Compared with text-to-video, reliability matters far more than spectacle. When choosing, check:
- Lip sync and voice alignment, including long sentences and proper nouns.
- Natural expressions and gestures, and whether long takes start to feel stiff.
- Multilingual voice-over, and whether lip movement is re-matched after switching language.
- What material a custom avatar requires, and how clear the consent process is.
That last point matters most. Building an avatar from a real person’s face or voice requires that person’s explicit consent. Reputable platforms usually ask for a consent video or a signed agreement. A tool that is casual about this is a warning sign.
Practical issues you can’t skip
Whatever category you choose, think about the following up front.
Commercial rights. Platforms and plans differ on whether generated content can be used commercially. Some free tiers allow personal use only; some require an AI-generated label. Read the terms before using output in a paid project.
Content labelling. A growing number of platforms and regulations require visible labels on AI-generated audio and video. Check the rules of the platform you publish to so your work isn’t throttled or removed.
Cost structure. Video generation is usually billed in credits or by duration, and HD, longer clips and high-quality modes cost more. Real projects often need many generations to get one usable shot, so budget for the discarded takes.
Workflow fit. One tool rarely produces a finished video. A common pipeline is a language model for the script, an image tool for key frames, a video model for shots, and editing software for assembly, music and subtitles. Check that a tool’s export formats and resolutions slot into the rest of your process.
A sample end-to-end workflow
Take a product promo of about one minute as an example. The work can be split like this.
Start by having a language model turn the product’s selling points into a shot list, with the content, duration, framing and voice-over for each shot. Next, create key frames for each shot with an image tool, keeping characters, colour palette and style consistent; this step sets the overall look of the finished piece. Then feed the key frames one by one to a video model, generating several versions per shot and keeping the best.
For segments where a presenter speaks to camera, use a digital-human platform and cut those clips in with the rest. Finish in your editing software with assembly, grading, music, subtitles and an AI-generated label. The more time you spend on the script and key frames, the less rework you’ll face later.
Recommendations by scenario
For short-form creative work or ad drafts, trial two or three text-to-video and image-to-video platforms side by side, focusing on image quality and stability. For e-commerce or product showcases, image-to-video with first-and-last-frame control is usually less hassle. If you already have footage, start with editing and effects tools. If you need explainers, training or multilingual content at volume, a digital-human platform is the most direct route.
These tools improve quickly, and today’s weakness may be fixed in the next release. Building your own test kit is more useful than memorising a ranking: keep a few fixed prompts and source assets, rerun them whenever a tool updates, and the answer will become clear.
