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
Published: 2025-12-15 13:30:51

Abstract: 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.