Hunyuan Motion [1B]
Hunyuan Motion 1B: text-driven animation for everyone.
Hunyuan Motion 1B is a text-to-motion model that creates short animated sequences from simple text descriptions. It specializes in plausible, everyday movements like walking, waving, and jumping, making it ideal for quick animation prototypes, storyboards, or adding motion to static characters.
Use it when you need to visualize a movement concept fast — for game development, pre-visualization, or social media content. The model handles common actions reliably and keeps outputs consistent with your text prompt.
Its key strength is simplicity: one text prompt, one motion output. You don't need rigging, keyframes, or timelines. Just describe the action in plain English and iterate rapidly.
- Animate a character walking or running
- Create a waving or greeting animation
- Show a simple dance or jump sequence
- Visualize a lifting or throwing motion
- Add movement to a static character concept
You describe the motion in plain words, and it generates a matching animation.
Generate many motion variations quickly to explore ideas.
Produces clean, predictable motions for common actions.
- Keep prompts simple: 'a person walks forward' works better than long descriptions.
- Specify direction (left, right, up, down) to control the motion path.
- Use present-tense verbs like 'jumps' or 'waves' for clearer results.
- Iterate with slight word changes rather than rewriting prompts entirely.
- Complex or multi-step actions may confuse the model.
- Output quality degrades for highly stylized or unrealistic motions.
- Cannot generate facial expressions or detailed hand movements reliably.
A hero leaps off a platform into action
A dancer twirls elegantly in a circle
A person pumps their fists and cheers
Set a number to reproduce the same motion for a given prompt.
Number of processing steps: more steps can improve quality but take longer.
Nucleus sampling: limits the pool of next-possible tokens to a probability mass.
Length of the generated animation clip in seconds.
Controls randomness: lower = more predictable, higher = more varied.
How closely the output follows the prompt: higher = stricter, lower = more creative.
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Do I need animation experience to use this?
No, you just describe the motion in everyday language.
Can I control the length of the animation?
The model outputs a fixed short clip, but you can chain multiple prompts for longer sequences.
Does it work with human characters only?
It works best with human-like figures, but you can try other anthropomorphic forms too.