KlingAvatar 2.0 Technical Report
Authors: name: Kling Team, name: Jialu Chen, name: Yikang Ding, name: Zhixue Fang, name: Kun Gai, name: Yuan Gao, name: Kang He, name: Jingyun Hua, name: Boyuan Jiang, name: Mingming Lao, name: Xiaohan Li, name: Hui Liu, name: Jiwen Liu, name: Xiaoqiang Liu, name: Yuan Liu, name: Shun Lu, name: Yongsen Mao, name: Yingchao Shao, name: Huafeng Shi, name: Xiaoyu Shi, name: Peiqin Sun, name: Songlin Tang, name: Pengfei Wan, name: Chao Wang, name: Xuebo Wang, name: Haoxian Zhang, name: Yuanxing Zhang, name: Yan Zhou
Organization: Kling Team

Avatar video generation models have achieved remarkable progress in recent years. However, prior work exhibits limited efficiency in generating long-duration high-resolution videos, suffering from temporal drifting, quality degradation, and weak prompt following as video length increases. To address these challenges, we propose KlingAvatar 2.0, a spatio-temporal cascade framework that performs upscaling in both spatial resolution and temporal dimension. The framework first generates low-resolution blueprint video keyframes that capture global semantics and motion, and then refines them into high-resolution, temporally coherent sub-clips using a first-last frame strategy, while retaining smooth temporal transitions in long-form videos. To enhance cross-modal instruction fusion and alignment in extended videos, we introduce a Co-Reasoning Director composed of three modality-specific large language model (LLM) experts. These experts reason about modality priorities and infer underlying user intent, converting inputs into detailed storylines through multi-turn dialogue. A Negative Director further refines negative prompts to improve instruction alignment. Building on these components, we extend the framework to support ID-specific multi-character control. Extensive experiments demonstrate that our model effectively addresses the challenges of efficient, multimodally aligned long-form high-resolution video generation, delivering enhanced visual clarity, realistic lip-teeth rendering with accurate lip synchronization, strong identity preservation, and coherent multimodal instruction following.