I’ve been exploring different AI frameworks for video generation, and most of them either feel too robotic or they break when you try to preserve a character’s identity. Then I tried Wan2.2-Animate, built by Tongyi Wanxiang, and it instantly felt like a step forward.
At its core, it’s really just two things. One model takes a static photo and gives it motion, expression, even a sense of presence. The other swaps out a character in a video while somehow keeping the original environment untouched. Simple ideas, but the results feel different.
Wan2.2-Animate-Move is where the fun begins. Upload a picture, give it a reference video, and suddenly your character can dance, jump, or just smile naturally. The expressions are convincing, the body movements flow instead of jerking, and the character still looks like the same person. For creators, it’s almost like having motion capture without the studio cost.
Wan2.2-Animate-Mix is the tool that gets people’s attention. Imagine taking a film clip and sliding yourself—or any image—into the scene, while the lighting, shadows, and tone all stay intact. The blend is seamless. Ads, fan edits, virtual influencers… the possibilities here feel endless.
Of course, the platform is flexible. It lets you pick between wan-std for quick and affordable outputs, or wan-pro when you need production-level quality. Uploads are simple: standard image formats like JPG or PNG, short video clips up to 30 seconds. That’s it. No steep learning curve.
What I like most is the balance. It isn’t just a demo tool that looks good in one example. It actually works across a range of scenarios: dance recreation, film reenactments, advertising, even multi-character swaps.
If you’re curious, check it out here: Wan Animate. I think it’s one of those frameworks that make you rethink what “AI video” really means, less about gimmicks and more about giving creators an actual toolbox.
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