We present MeshLoom, a feed-forward registration network that directly reconstructs vertex deformations across mesh sequences. Our approach advances non-rigid registration beyond existing models, which are typically constrained by costly per-instance optimization, narrow object categories, pairwise-only inputs, or merely intermediate outputs. The network is simple and efficient, registering multiple meshes within seconds. At its core lies a topology-aware encoder--decoder design. Specifically, we first introduce a topology-aware point representation that encodes the anchor (reference) mesh's topology into its per-vertex features. This representation strengthens the network's understanding of the anchor-mesh geometry and disambiguates points that are Euclidean-close yet geodesically distant. We then propose a multi-modal encoder that fuses this anchor-mesh representation with complementary cues from each frame, such as shape latents and image features. These multi-source signals are compressed into a compact global motion embedding that captures dense inter-frame correspondence. A lightweight decoder then queries this global embedding with the anchor-mesh point representation, retrieving per-vertex deformations at target timestamps. Through extensive experiments across diverse motions and object categories, we show that MeshLoom achieves state-of-the-art results on non-rigid registration. In addition, we find that our global embedding-then-query paradigm naturally enables the network to generate deformations at intermediate timestamps, which extends MeshLoom to motion interpolation and mesh morphing. Project page: https://meshloom.github.io/ .

翻译：我们提出MeshLoom——一种前馈式配准网络，可直接重建网格序列上的顶点形变。该方法将非刚性配准推至超越现有模型的水平。现有模型通常受限于昂贵的逐实例优化、狭窄的物体类别、仅针对成对输入，或仅输出中间结果。本网络简洁高效，可在数秒内完成多个网格的配准。其核心在于一种拓扑感知的编码器-解码器设计。具体而言，我们首先引入一种拓扑感知的点表示方法，将锚点（参考）网格的拓扑结构编码至每个顶点特征中。这种表示增强了网络对锚点网格几何结构的理解，并区分了欧氏距离近但测地距离远的点。进而，我们提出一种多模态编码器，将该锚点网格表示与每一帧中的补充线索（如形状潜变量和图像特征）进行融合。这些多源信号被压缩成一个紧凑的全局运动嵌入，捕获密集的帧间对应关系。随后，轻量级解码器通过锚点网格点表示查询该全局嵌入，在目标时间戳提取每个顶点的形变。通过针对多样化运动与物体类别的大量实验表明，MeshLoom在非刚性配准上达到了最先进的性能。此外，我们发现全局嵌入-查询范式天然地使网络能够生成中间时间戳的形变，从而将MeshLoom扩展至运动插值与网格变形。项目页面：https://meshloom.github.io/ 。