KlingAvatar 2.0 Technical Report
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.
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Kling Team
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Jialu Chen
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Yikang Ding
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Zhixue Fang
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Kun Gai
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Yuan Gao
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Kang He
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Jingyun Hua
claimed_verified
2025-12-22T11:01:06.094Z
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jingyun
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Boyuan Jiang
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Mingming Lao
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Xiaohan Li
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Hui Liu
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Jiwen Liu
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Xiaoqiang Liu
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Yuan Liu
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Shun Lu
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Yongsen Mao
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Yingchao Shao
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Huafeng Shi
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Xiaoyu Shi
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Peiqin Sun
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Songlin Tang
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Pengfei Wan
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Chao Wang
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Xuebo Wang
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Haoxian Zhang
claimed_verified
2026-03-04T09:25:15.947Z
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Yuanxing Zhang
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Yan Zhou
KlingAvatar 2.0 Technical Report
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.
KlingAvatar 2.0 addresses inefficiencies in generating long-duration, high-resolution videos by using a spatio-temporal cascade framework with a Co-Reasoning Director and Negative Director for improved multimodal instruction alignment.
spatio-temporal cascade blueprint video keyframes high-resolution temporally coherent sub-clips first-last frame strategy smooth temporal transitions Co-Reasoning Director modality-specific large language model multi-turn dialogue Negative Director multimodal instruction following identity preservation lip synchronization