We present LiftAvatar, a new paradigm that completes sparse monocular observations in kinematic space (e.g., facial expressions and head pose) and uses the completed signals to drive high-fidelity avatar animation. LiftAvatar is a fine-grained, expression-controllable large-scale video diffusion Transformer that synthesizes high-quality, temporally coherent expression sequences conditioned on single or multiple reference images. The key idea is to lift incomplete input data into a richer kinematic representation, thereby strengthening both reconstruction and animation in downstream 3D avatar pipelines. To this end, we introduce (i) a multi-granularity expression control scheme that combines shading maps with expression coefficients for precise and stable driving, and (ii) a multi-reference conditioning mechanism that aggregates complementary cues from multiple frames, enabling strong 3D consistency and controllability. As a plug-and-play enhancer, LiftAvatar directly addresses the limited expressiveness and reconstruction artifacts of 3D Gaussian Splatting-based avatars caused by sparse kinematic cues in everyday monocular videos. By expanding incomplete observations into diverse pose-expression variations, LiftAvatar also enables effective prior distillation from large-scale video generative models into 3D pipelines, leading to substantial gains. Extensive experiments show that LiftAvatar consistently boosts animation quality and quantitative metrics of state-of-the-art 3D avatar methods, especially under extreme, unseen expressions.

翻译：我们提出LiftAvatar，这是一种在运动学空间（如面部表情与头部姿态）中补全稀疏单目观测数据，并利用补全后的信号驱动高保真化身动画的新范式。LiftAvatar是一个细粒度、表情可控的大规模视频扩散Transformer，能够基于单张或多张参考图像合成高质量、时序连贯的表情序列。其核心思想是将不完整的输入数据提升为更丰富的运动学表征，从而增强下游三维化身流程的重建与动画效果。为此，我们引入了（i）结合着色图与表情系数的多粒度表情控制方案，实现精准稳定的驱动；（ii）多参考帧条件聚合机制，从多帧图像中整合互补信息，确保强三维一致性与可控性。作为即插即用的增强模块，LiftAvatar直接解决了基于三维高斯泼溅的化身在日常单目视频中因运动学线索稀疏而导致的表达能力受限与重建伪影问题。通过将不完整观测扩展为多样化的姿态-表情变化，LiftAvatar还能实现从大规模视频生成模型到三维流程的有效先验知识蒸馏，带来显著性能提升。大量实验表明，LiftAvatar能持续提升当前最先进三维化身方法的动画质量与量化指标，尤其在极端、未见过的表情条件下表现优异。