AutoKeyframe: Autoregressive Keyframe Generation for Human Motion Synthesis and Editing

摘要

Keyframing has long been the cornerstone of standard character animation pipelines, offering precise control over detailed postures and dynamics. However, this approach is labor-intensive, necessitating significant manual effort. Automating this process while balancing the trade-off between minimizing manual input and maintaining full motion control has therefore been a central research challenge. In this work, we introduce AutoKeyframe, a novel framework that simultaneously accepts dense and sparse control signals for motion generation by generating keyframes directly. Dense signals govern the overall motion trajectory, while sparse signals define critical key postures at specific timings. This approach substantially reduces manual input requirements while preserving precise control over motion. The generated keyframes can be easily edited to serve as detailed control signals. AutoKeyframe operates by automatically generating keyframes from dense root positions, which can be determined through arc-length parameterization of the trajectory curve. This process is powered by an autoregressive diffusion model, which facilitates keyframe generation and incorporates a skeleton-based gradient guidance technique for sparse spatial constraints and frame editing. Extensive experiments demonstrate the efficacy of AutoKeyframe, achieving high-quality motion synthesis with precise and intuitive control.