Video-guided 3D animation holds immense potential for content creation, offering intuitive and precise control over dynamic assets. However, practical deployment faces a critical yet frequently overlooked hurdle: the pose misalignment dilemma. In real-world scenarios, the initial pose of a user-provided static mesh rarely aligns with the starting frame of a reference video. Naively forcing a mesh to follow a mismatched trajectory inevitably leads to severe geometric distortion or animation failure. To address this, we present Rectified Dynamic Mesh (R-DMesh), a unified framework designed to generate high-fidelity 4D meshes that are ``rectified'' to align with video context. Unlike standard motion transfer approaches, our method introduces a novel VAE that explicitly disentangles the input into a conditional base mesh, relative motion trajectories, and a crucial rectification jump offset. This offset is learned to automatically transform the arbitrary pose of the input mesh to match the video's initial state before animation begins. We process these components via a Triflow Attention mechanism, which leverages vertex-wise geometric features to modulate the three orthogonal flows, ensuring physical consistency and local rigidity during the rectification and animation process. For generation, we employ a Rectified Flow-based Diffusion Transformer conditioned on pre-trained video latents, effectively transferring rich spatio-temporal priors to the 3D domain. To support this task, we construct Video-RDMesh, a large-scale dataset of over 500k dynamic mesh sequences specifically curated to simulate pose misalignment. Extensive experiments demonstrate that R-DMesh not only solves the alignment problem but also enables robust downstream applications, including pose retargeting and holistic 4D generation.

翻译：视频引导的三维动画在内容创作领域具有巨大潜力，能够对动态资产实现直观且精准的控制。然而，实际部署面临一个关键却常被忽视的障碍：姿态错位困境。在真实场景中，用户提供的静态网格初始姿态与参考视频的起始帧几乎难以对齐。强行迫使网格遵循不匹配的运动轨迹，必然导致严重的几何畸变或动画失败。为应对这一挑战，我们提出修正动态网格（R-DMesh）——一个统一框架，旨在生成与视频上下文对齐的“修正型”高保真4D网格。与标准运动迁移方法不同，本方法引入新型变分自编码器，显式地将输入解耦为条件性基准网格、相对运动轨迹，以及关键的修正跳跃偏移量。该偏移量通过自动学习，在动画生成前将输入网格的任意姿态变换至匹配视频初始状态。我们通过三流注意力机制处理这些组件：该机制利用逐顶点几何特征调节三个正交流，确保修正与动画过程中的物理一致性与局部刚体性。在生成方面，我们采用基于修正流的扩散Transformer，以预训练视频潜变量为条件，有效将丰富时空先验迁移至三维领域。为支撑本任务，我们构建了Video-RDMesh——包含超50万条动态网格序列的大规模数据集，专门模拟姿态错位场景。大量实验表明，R-DMesh不仅能解决对齐问题，还可实现姿态重定向与整体4D生成等鲁棒下游应用。