Kling-Avatar: Grounding Multimodal Instructions for Cascaded
  Long-Duration Avatar Animation Synthesis
Authors: name: Yikang Ding, name: Jiwen Liu, name: Wenyuan Zhang, name: Zekun Wang, name: Wentao Hu, name: Liyuan Cui, name: Mingming Lao, name: Yingchao Shao, name: Hui Liu, name: Xiaohan Li, name: Ming Chen, name: Xiaoqiang Liu, name: Yu-Shen Liu, name: Pengfei Wan

Recent advances in audio-driven avatar video generation have significantly
enhanced audio-visual realism. However, existing methods treat instruction
conditioning merely as low-level tracking driven by acoustic or visual cues,
without modeling the communicative purpose conveyed by the instructions. This
limitation compromises their narrative coherence and character expressiveness.
To bridge this gap, we introduce Kling-Avatar, a novel cascaded framework that
unifies multimodal instruction understanding with photorealistic portrait
generation. Our approach adopts a two-stage pipeline. In the first stage, we
design a multimodal large language model (MLLM) director that produces a
blueprint video conditioned on diverse instruction signals, thereby governing
high-level semantics such as character motion and emotions. In the second
stage, guided by blueprint keyframes, we generate multiple sub-clips in
parallel using a first-last frame strategy. This global-to-local framework
preserves fine-grained details while faithfully encoding the high-level intent
behind multimodal instructions. Our parallel architecture also enables fast and
stable generation of long-duration videos, making it suitable for real-world
applications such as digital human livestreaming and vlogging. To
comprehensively evaluate our method, we construct a benchmark of 375 curated
samples covering diverse instructions and challenging scenarios. Extensive
experiments demonstrate that Kling-Avatar is capable of generating vivid,
fluent, long-duration videos at up to 1080p and 48 fps, achieving superior
performance in lip synchronization accuracy, emotion and dynamic
expressiveness, instruction controllability, identity preservation, and
cross-domain generalization. These results establish Kling-Avatar as a new
benchmark for semantically grounded, high-fidelity audio-driven avatar
synthesis.