用视频驱动3D模型动画，无需骨骼或绑定信息
BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives
- 用可学习的刚性运动基元组合生成动画，不依赖骨骼或权重
- 在单目视频上实现高精度、时间稳定的3D网格动画
- 适合需要快速生成动作的3D内容创作者
我们提出BLARM，一种用于视频驱动3D网格动画的前馈方法。给定单目视频和静态物体网格，BLARM预测一个时序连贯的动画网格，其运动与视频一致。不同于依赖显式骨架或直接回归高维顶点运动的方法，我们使用一组学习得到的、随时间变化的刚性运动基元和时不变的顶点-基元绑定权重来表示动画，从而在不需骨架、笼状结构、绑定权重或骨架标注的情况下，构建低维变形空间。架构通过分解的空间-时间注意力将几何衍生的变形隐变量与视频特征对齐，再通过预测的绑定权重混合刚性变换进行解码。训练采用轨迹重建、熵正则化和运动感知对比学习，使BLARM在单目视频上生成准确且时间稳定动画的同时，恢复出紧凑且可解释的运动结构。
原文摘要 · Abstract (English)
We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a temporally coherent animated mesh whose motion follows the video. Rather than relying on explicit rigs or directly regressing high-dimensional vertex motion, we represent animation using a compact set of learned, time-varying rigid motion components and time-invariant vertex-to-component skinning weights. This yields a low-dimensional deformation space without requiring skeletons, cages, skinning weights, or rig annotations. Our architecture conditions geometry-derived deformation latents on video features through factorized spatial-temporal attention, then decodes rigid transformations blended by predicted skinning weights. Trained with trajectory reconstruction, entropy regularization, and motion-aware contrastive learning, BLARM produces accurate and temporally stable animations while recovering compact, interpretable motion structure from monocular video.
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