Kling-MotionControl Technical Report
Kling Team, Jialu Chen, Yikang Ding, Zhixue Fang, Kun Gai, Kang He, Xu He, Jingyun Hua, Mingming Lao, Xiaohan Li, Hui Liu, Jiwen Liu, Xiaoqiang Liu, Fan Shi, Xiaoyu Shi, Peiqin Sun, Songlin Tang, Pengfei Wan, Tiancheng Wen, Zhiyong Wu, Haoxian Zhang, Runze Zhao
2026-03-04
Summary
This paper introduces Kling-MotionControl, a new system for creating realistic character animations from a video or text instructions. It aims to make characters move in a lifelike way by taking motion from one source and applying it to a still image of a character.
What's the problem?
Creating convincing character animation is difficult because you need to make sure the whole body moves naturally, including details like facial expressions and hand movements. Existing methods often struggle to balance large movements with small, expressive ones, and they don't always work well when you want to apply the same motion to different characters. Also, these methods can be slow, making them impractical for real-time use.
What's the solution?
Kling-MotionControl tackles this by breaking down the animation process into parts – body, face, and hands – and using different techniques for each. It learns to ignore specific character features so it can transfer motion to a wide variety of characters, from realistic people to cartoon figures, while still keeping the character looking like themselves. They also sped up the process significantly using a technique called distillation, making it much faster. Finally, the system understands what you *want* the character to do, not just copying movements from a video, allowing for control through text prompts.
Why it matters?
This research is important because it provides a more effective and efficient way to create high-quality character animations. It’s better than current commercial and open-source options, offering more realistic movement, working with diverse characters, and being much faster. This could have a big impact on fields like filmmaking, video games, and virtual reality, making it easier and cheaper to create compelling animated content.
Abstract
Character animation aims to generate lifelike videos by transferring motion dynamics from a driving video to a reference image. Recent strides in generative models have paved the way for high-fidelity character animation. In this work, we present Kling-MotionControl, a unified DiT-based framework engineered specifically for robust, precise, and expressive holistic character animation. Leveraging a divide-and-conquer strategy within a cohesive system, the model orchestrates heterogeneous motion representations tailored to the distinct characteristics of body, face, and hands, effectively reconciling large-scale structural stability with fine-grained articulatory expressiveness. To ensure robust cross-identity generalization, we incorporate adaptive identity-agnostic learning, facilitating natural motion retargeting for diverse characters ranging from realistic humans to stylized cartoons. Simultaneously, we guarantee faithful appearance preservation through meticulous identity injection and fusion designs, further supported by a subject library mechanism that leverages comprehensive reference contexts. To ensure practical utility, we implement an advanced acceleration framework utilizing multi-stage distillation, boosting inference speed by over 10x. Kling-MotionControl distinguishes itself through intelligent semantic motion understanding and precise text responsiveness, allowing for flexible control beyond visual inputs. Human preference evaluations demonstrate that Kling-MotionControl delivers superior performance compared to leading commercial and open-source solutions, achieving exceptional fidelity in holistic motion control, open domain generalization, and visual quality and coherence. These results establish Kling-MotionControl as a robust solution for high-quality, controllable, and lifelike character animation.