AniMimic：从视频先验中模仿三维动画
AniMimic: Imitating 3D Animation from Video Priors
把视频扩散模型当作运动先验，通过可微渲染反推网格的骨骼与关节参数，再用可微软体仿真细化形变；这条「视频先验→可微渲染监督→物理细化」的管线可直接迁移到单目视频驱动网格动画、4D 重建与角色动画生成。
原文依据 animates static 3D meshes using motion priors learned from video diffusion models
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论创建逼真的三维动画仍然是一个耗时且依赖专业知识的流程，需要手工绑定骨骼、设置关键帧并微调复杂运动。与此同时，视频扩散模型近期展现出显著的二维运动想象能力，能够根据文本或图像提示生成动态且视觉连贯的运动。然而，其输出缺乏显式的三维结构，无法直接用于动画或仿真。本文提出 AniMimic，一个利用从视频扩散模型中学习到的运动先验来驱动静态三维网格动画的框架。从输入网格出发，AniMimic 合成单目动画视频，自动构建带蒙皮权重的骨架，并通过可微渲染与基于视频的监督来细化其关节参数。为进一步提升真实感，我们引入可微仿真模块，通过基于物理的软组织动力学细化网格形变。本方法在视频扩散的创造力与三维绑定动画的结构控制之间架起桥梁，生成物理上合理、时间上连贯且可由美术人员编辑的运动序列，并能无缝集成到标准动画管线中。
英文原文摘要 1206 字符
Creating realistic 3D animation remains a time-consuming and expertise-dependent process, requiring manual rigging, keyframing, and fine-tuning of complex motions. Meanwhile, video diffusion models have recently demonstrated remarkable 2D motion imagination, generating dynamic and visually coherent motion from text or image prompts. However, their outputs lack explicit 3D structure and cannot be directly used for animation or simulation. We present AniMimic, a framework that animates static 3D meshes using motion priors learned from video diffusion models. Starting from an input mesh, AniMimic synthesizes a monocular animation video, automatically constructs a skeleton with skinning weights, and refines its joint parameters through differentiable rendering and video-based supervision. To further enhance realism, we integrate a differentiable simulation module that refines mesh deformation through physically grounded soft-tissue dynamics. Our method bridges the creativity of video diffusion and the structural control of 3D rigged animation, producing physically plausible, temporally coherent, and artist-editable motion sequences that integrate seamlessly into standard animation pipelines.