This is a simplified guide to an AI model called kling-v2.6-motion-control maintained by kwaivgi. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
kling-v2.6-motion-control specializes in creating videos where character movements and expressions follow precise instructions from reference materials. This model differs from earlier versions like kling-v2.1 and kling-v2.0 by adding motion control capabilities, allowing you to dictate exactly how characters move rather than generating motion based solely on prompts. Unlike kling-v1.5-pro, which focuses on general video generation quality, this version prioritizes character action precision. The kling-v2.1-master remains a premium option for broader video generation, while this model targets specific animation and choreography needs.
Model inputs and outputs
The model accepts a reference image and optional video alongside text instructions, then produces a video where characters perform actions matching your specifications. You can control whether the generated video matches the orientation of characters in your reference image or follows the motion from a reference video. The model supports two quality tiers and allows you to preserve or replace audio in the final output.
Inputs
- Prompt: Text description of the video you want to generate, including any motion effects or new elements to add
- Image: Reference image showing the character and scene (JPG, PNG, max 10MB, dimensions 340px-3850px, aspect ratio 1:2.5 to 2.5:1)
- Video: Optional reference video showing desired character actions (MP4, MOV, max 100MB, 3-30 seconds)
- Character orientation: Choose between matching the image orientation (max 10s video) or matching a reference video's orientation (max 30s video)
- Mode: Select standard mode for cost-effective generation or professional mode for higher quality
- Keep original sound: Toggle to preserve or replace audio from your reference materials
Outputs
- Output: A video file with your generated animation based on the reference image and motion specifications
Capabilities
The model interprets both visual references and text prompts to create consistent character movements. You can supply a still image and describe an action, and the model generates video showing that character performing the action. Alternatively, you can provide a video demonstrating the exact movements you want, and the model applies those movements to your reference character. Expression control comes from analyzing your starting image and maintaining consistency throughout the generated sequence. The extended duration options—up to 10 seconds with image orientation control and up to 30 seconds with video orientation matching—enable longer narrative sequences than simpler text-to-video approaches.
What can I use it for?
Content creators can use this for generating character animations for social media, marketing videos, or short films without traditional animation skills. Game developers can prototype character movements for cutscenes or in-game cinematics. Marketing teams can create personalized video content where a character performs specific actions tailored to campaigns. Educators can generate explanatory videos with animated characters demonstrating concepts. Virtual influencer creators can produce consistent character content by maintaining reference images and motion libraries. The model works for both professional-quality content and cost-effective rapid prototyping depending on which mode you select.
Things to try
Test the difference between providing a detailed motion video versus describing actions in text prompts—the model may prioritize one source differently. Experiment with character orientation settings depending on whether your reference image shows a front-facing character or profile view, as this affects how naturally the motion translates. Try combining simple reference images with complex motion descriptions to see how much movement variation the model adds beyond your reference video. Use the professional mode when generating content that needs to match existing brand or quality standards, reserving standard mode for internal iterations. Supply multiple reference videos showing different types of motion, then vary your prompts to see how the model generalizes movement patterns across new character poses.