BLARM：透過混合潛在剛體運動基本單元，從影片製作3D物體動畫

摘要

我們提出BLARM，一種用於影片驅動的3D網格動畫的前饋方法。給定單目影片和靜態物件網格，BLARM預測一個時間上連貫的動畫網格，其動作跟隨影片。我們不依賴顯式綁定或直接回歸高維頂點運動，而是使用一組緊湊的、學習到的隨時間變化的剛體運動分量，以及不隨時間變化的頂點到分量蒙皮權重來表示動畫。這產生了一個低維度變形空間，無需骨架、籠、蒙皮權重或綁定標註。我們的架構透過分解的空間-時間注意力，以影片特徵對幾何推導出的變形潛在特徵進行條件化，然後解碼由預測蒙皮權重混合的剛體變換。透過軌跡重建、熵正則化和動作感知對比學習進行訓練，BLARM在恢復緊湊、可解釋的動作結構的同時，從單目影片中產生準確且時間穩定的動畫。

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.