The most significant January update on the IMI platform is Kling 2.6 Motion Control. Here's how to use it

One of the most impactful January updates on the IMI service is Kling 2.6 Motion Control. It literally lets you control a character's movement frame by frame, transferring actions from real video to a static image. Previously, this level of editing required a filming crew, actors, and weeks of post-production. Now, it takes just a couple of files and a click of the "Generate" button.

In this article, we'll explore what Kling 2.6 Motion Control is, how it differs from standard image-to-video models, and how to get the best results for your content.

Welcome to the Era of Controlled AI Video

Kling 2.6 Motion Control is a specialized multimodal model that understands human body physics and cinematic camera movement logic. Simply put, the neural network no longer "guesses" how a character should move. It precisely replicates movement from a reference video and transfers it to your character while fully preserving their appearance.

The result is predictable, visually clean videos suitable for marketing, social media, and production.

What is Kling 2.6 Motion Control?

At its core, Motion Control is based on a simple yet powerful idea:

• You provide a reference image (your character).
• You add a reference motion video (what they are doing).
• The neural network combines them.

Movement, facial expressions, tempo, and weight distribution are taken from the video, while appearance and identity come from the image. Unlike previous image-to-video models, there's minimal AI "improvisation" here. Kling 2.6 acts as a digital "puppeteer," not an inventor.

Key Features of Kling 2.6 Motion Control

Complex Movements and Active Actions

The service confidently handles dancing, fight scenes, and athletic movements. The model understands body inertia and balance. If the reference video features a jump or a sharp kick, the generated character appears heavy and physically plausible, not "clay-like" or obviously AI-generated.

Precise Hand and Finger Movements

Hands are a common weak point in AI video, but this aspect is significantly improved here. Finger and hand motions replicate the real video, which is crucial for gestures, demonstrations, and product scenes.

Scene and Environment Freedom

The background from the reference video is not mandatory. You can change the surroundings using a text description while preserving the character's movement. For example, the character continues walking or dancing but in a different space.

Camera and Perspective Control

Kling 2.6 offers different camera orientation modes. You can define how strictly the AI should follow the camera movements from the video or adhere to the composition of the source image. This provides control over the frame's narrative.

How Motion Control Works in Practice

Simplifying it to a "for dummies" level, the process looks like this:

1. The image tells the neural network who is in the frame.
2. The video shows what they are doing.
3. Kling 2.6 carefully layers one onto the other without breaking anatomy or style.

How to Use Kling 2.6 Motion Control: Step-by-Step

Step 1: Prepare the Source Image

The result's quality directly depends on the image. Pay attention to two key points:

• Visible Limbs. If the image shows hands in pockets but the video features hand-waving, the neural network will have to "imagine" them, often leading to extra fingers or blurred forms.
• Free Space. Leave margin around the edges of the frame. If the character will move their arms widely or dance, they need space within the image.

Step 2: Choose the Motion Video

The reference video is the "skeleton" of the future animation.

The best results come from videos with: one clear character; a simple, contrasting background; and matching scale.

For a talking-head portrait, use a close-up shot. Applying a full-body walking video to a portrait might cause the face to "float" and jerk.

Step 3: Generation

After uploading the image and video, simply click Generate. The output is a ready-made video optimized for TikTok, Instagram, or YouTube. You can download and use it immediately.

Practical Use Cases

Virtual Influencers

Create a brand character and animate it using movements from real people. For example, company employees record videos, and the character replicates their gestures and expressions—no studio or camera required.

Product Demonstrations

Motion Control is excellent for hand-centric scenes: interacting with an interface, gadgets, or physical products. Movements look natural and clear.

Content Localization

Take one high-quality "hero" motion video and apply it to different characters across various age groups, appearances, and ethnicities. The movement remains the same, allowing easy content adaptation for different markets without reshooting.

Conclusion

Kling 2.6 Motion Control isn't just another update; it's a step towards high-quality, controlled video production. This is precisely why we prioritized its integration into the IMI platform as quickly as possible.

If before you had to adjust your plans to fit AI video results, now the results follow your commands. We hope this guide is helpful—and that social media gets flooded with a wave of awesome, viral video content.

Keywords: Kling 2.6 Motion Control, AI video generation, controlled AI video, motion transfer, image to video, video production, AI video editing, virtual influencers, product demonstration AI, IMI platform, AI video tool, character animation AI, AI for marketing.