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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, Yuanxing Zhang, Yan Zhou
TL;DR
Kling-MotionControl tackles the challenge of holistic character animation that must coordinate multiple motion granularities while preserving identity across diverse characters. It uses a unified DiT-based framework with specialized motion representations, adaptive cross-identity learning, identity fusion, subject references, and accelerated inference. The paper reports robust holistic animation, natural cross-identity transfer, preserved appearance, and more than 10× inference acceleration, while noting ethical risks surrounding realistic identity-preserving synthesis.
Problem
Existing holistic animation methods struggle to coordinate large-scale and fine-grained motion, preserve identity across diverse morphologies, and maintain practical inference efficiency.
Method
Kling-MotionControl unifies body, face, and hand motion representations with adaptive identity-agnostic learning, identity fusion, subject-library references, curated data, and multi-stage distillation.
Results
Kling-MotionControl achieves robust, precise, and expressive holistic character animation with natural cross-identity adaptation and consistent subject identity.
Takeaways & Limitations
The framework supports controllable character animation across diverse characters and motion scales within a unified system.
Takeaways & Limitations
Realistic identity-preserving synthesis raises risks of privacy violations, unauthorized likeness appropriation, and deceptive deepfake media.
Abstract
from arXiv · showhide
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.
1 Introduction
Kling-MotionControl addresses the difficulty of coordinating body, facial, and hand motion while preserving identity across diverse characters. It combines multi-granular motion modeling, identity-aware transfer, text and camera control, curated data, and accelerated inference.
- Motivation: Kling-MotionControl targets holistic animation that preserves precise motion control and reference appearance across novel characters.Prior methods struggle to balance limb stability, fine-grained articulation, identity consistency, and inference efficiency.
- Unified Multi-Granularity Motion Orchestration: Its divide-and-conquer framework coordinates heterogeneous body, face, and hand representations within one model.Progressive multi-stage training combines large-scale structural stability with facial and finger expressiveness across close-ups and full-body scenes.
- Adaptive Cross-Identity Motion Transfer: Identity-agnostic geometric and semantic motion learning enables natural retargeting across major morphological differences without manual calibration.The approach supports transfers such as adult-to-child and human-to-animal while preserving the driving action’s high-level intent.
- Faithful Identity Preservation: Identity encoding, fusion, and a subject library preserve appearance during extreme poses and long-duration generation.The subject library accepts additional references, including multi-view images or video clips, to construct a more robust identity representation.
- Text and Camera Control: Text prompts control scene elements, clothing, and camera movements, while 3D-aware representations support free-view rendering with dynamic camera trajectories.Large-scale multi-view supervision provides geometric consistency beyond simple 2D alignment.
- High-Efficiency Inference Acceleration: 10×+ end-to-end acceleration is achieved through dual-branch sampling, multi-stage distillation, and conditional-gradient merging.The resulting few-step student model reduces sampling overhead while preserving model performance.
2 Evaluation
The evaluation combines human preference testing across 150 cases with visual comparisons against commercial and open-source baselines. Kling-MotionControl surpasses competitors across all reported dimensions and shows strong qualitative fidelity in expressions, gestures, body motion, and identity preservation.
- Evaluation Protocol: 150 high-quality test cases paired reference images with driving videos from distinct subjects for human preference evaluation.Participants made pairwise Good/Same/Bad judgments against baseline methods.
- Evaluation Protocol: GSB evaluation covered Overall Performance, Visual Quality, Dynamic Quality, Identity Preservation, Motion Accuracy, and Expression Accuracy.These dimensions span frame-level quality, temporal consistency, appearance retention, body-motion transfer, and facial dynamics.
- Quantitative Results: Kling-MotionControl surpassed Dreamina, Runway Act-Two, and Wan-Animate across every evaluation dimension.The comparison used 1080P resolution, equal video duration, aligned prompts, and each method’s recommended inference configuration.
- Results Presentation: Table 1 reports GSB metrics across diverse criteria, with Qual., Preserv, and Acc. denoting Quality, Preservation, and Accuracy.Figure 3 visualizes preference rates as percentages and omits labels for categories with 0%.
- Qualitative Results: Qualitative comparisons show more precise facial expressions and hand gestures, plus stronger robustness to complex and rapid body motion.Baseline methods exhibited limited expressiveness, erroneous hand movements, artifacts, or failures under difficult facial and hand dynamics.
- Qualitative Results: Generated results span close-up portraits to full-body views while coordinating large-scale motion with facial, lip, and hand details across diverse scenarios.The visualizations also emphasize appearance preservation across identities and text controllability.
3 Related Work
Related work progresses from specialized facial or body animation toward holistic full-body systems. Existing approaches still face challenges coordinating motion granularity, identity transfer, and visual fidelity, motivating unified designs such as Kling-MotionControl.
- Background and Body Animation: Video diffusion models evolved from image-based U-Nets with temporal modules toward controllable human-centric video generation and DiT backbones.These developments established the generative foundation for character animation.
- Body Animation: Body animation methods have used optical flow, 2D skeletal poses, DensePose, and SMPL-based 3D priors to control motion and enforce structure.The field also expanded toward complex human-scene interactions.
- Holistic Full-Body Animation: Qualitative comparisons position Kling-MotionControl as producing expressive, motion-accurate videos with faithful identity and scene consistency.The figure contrasts facial and hand-detail quality as well as robustness to complex body motion.
- Facial Animation: Facial animation progressed from GAN-based keypoints and parametric models to diffusion methods improving real-time performance, quality, and cross-identity reenactment.Examples include LivePortrait, XPortrait, and SkyReels-A1.
- Holistic Full-Body Animation: Holistic systems including Wan-Animate, X-UniMotion, and DreamActor-M1 still struggle to coordinate varying motion granularities and disentangle identity from motion.These limitations are associated with visual artifacts and identity drift in challenging settings.
4 Conclusion
Kling-MotionControl unifies heterogeneous motion representations, adaptive cross-identity transfer, identity-preserving reference support, acceleration, and prompt enhancement. The reported results establish it as a robust, precise, expressive, efficient, and controllable holistic animation framework.
- Conclusion: Kling-MotionControl coordinates body, facial, and hand representations to combine structural stability with fine-grained articulatory expressiveness.Adaptive motion learning supports diverse-character transfer, while identity encoding, fusion, and a subject library preserve appearance.
- Conclusion: Inference efficiency improves by over 10× through advanced acceleration strategies, alongside intelligent prompt enhancement for multi-conditional controllability.The conclusion links these capabilities to practical utility.
Impact Statement
The paper identifies both creative potential and ethical risks for highly controllable character animation. It emphasizes responsible safeguards and governance to reduce misuse while protecting individual rights and societal trust.
- Impact and Risks: Character animation may support digital entertainment, virtual reality, and creative content production, but realistic synthesis raises ethical concerns.The stated concerns include privacy violations, unauthorized likeness appropriation, and deceptive deepfake media.
- Impact and Risks: Animating individuals without consent could synthesize actions or statements they never performed.The paper calls for technical safeguards alongside ethical guidelines and legal frameworks.
- Responsible Use: Individuals and characters shown in the paper are presented solely for scientific visualization and technical illustration.
- Responsible Use: The authors advocate content filtering and watermarking to help prevent misuse while maintaining responsible research practices.They state that data processing and model development follow strict ethical standards.