Kling-MotionControl Technical Report

TL;DR

Kling-MotionControl is a comprehensive framework that enables high-fidelity, controllable, and expressive character animation with robust cross-identity generalization and accelerated inference, surpassing existing solutions in quality and flexibility.

Contribution

The paper introduces Kling-MotionControl, a novel DiT-based system that integrates heterogeneous motion representations, adaptive identity-agnostic learning, and multi-stage distillation for efficient, expressive, and generalizable character animation.

Findings

Outperforms leading commercial and open-source solutions in fidelity and control.

Achieves over 10x faster inference speed through multi-stage distillation.

Demonstrates robust generalization across diverse character types and styles.

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.