DragMesh: 交互式3D生成变得容易
DragMesh: Interactive 3D Generation Made Easy
AI总结：
DragMesh通过解耦运动学推理和运动生成，实现实时交互式3D生成，无需重新训练即可在新对象上生成合理运动。
摘要：
尽管生成模型在创建静态3D内容方面表现出色，但追求能够理解物体运动和响应交互的系统仍是一个根本性挑战。当前的方法在关节运动方面处于十字路口：它们要么在物理上一致但速度太慢，无法用于实时应用，要么生成性强但违反基本的运动学约束。我们提出了DragMesh，一个基于轻量级运动生成核心的实时交互3D关节生成框架。我们的核心贡献是一种新的解耦运动学推理和运动生成框架。首先，我们通过解耦语义意图推理（确定关节类型）和几何回归（使用我们的运动学预测网络（KPP-Net）确定轴和原点）来推断潜在的关节参数。其次，为了利用双四元数表示刚体运动的紧凑性、连续性和无奇点性，我们开发了一种新的双四元数变分自编码器（DQ-VAE）。该DQ-VAE接收这些预测的先验，以及原始用户的拖动输入，以生成完整的、合理的运动轨迹。为确保严格遵守运动学，我们通过FiLM（特征加权线性调制）条件在DQ-VAE的非自回归Transformer解码器的每一层中注入关节先验。这种持续的、多尺度的指导由数值稳定的叉积损失补充，以确保轴对齐。这种解耦设计使DragMesh能够实现实时性能，并在无需重新训练的情况下在新对象上生成合理的、生成性的关节运动，为生成3D智能提供了一个实用的步骤。代码：https://github.com/AIGeeksGroup/DragMesh。网站：https://aigeeksgroup.github.io/DragMesh。
英文摘要：
While generative models have excelled at creating static 3D content, the pursuit of systems that understand how objects move and respond to interactions remains a fundamental challenge. Current methods for articulated motion lie at a crossroads: they are either physically consistent but too slow for real-time use, or generative but violate basic kinematic constraints. We present DragMesh, a robust framework for real-time interactive 3D articulation built around a lightweight motion generation core. Our core contribution is a novel decoupled kinematic reasoning and motion generation framework. First, we infer the latent joint parameters by decoupling semantic intent reasoning (which determines the joint type) from geometric regression (which determines the axis and origin using our Kinematics Prediction Network (KPP-Net)). Second, to leverage the compact, continuous, and singularity-free properties of dual quaternions for representing rigid body motion, we develop a novel Dual Quaternion VAE (DQ-VAE). This DQ-VAE receives these predicted priors, along with the original user drag, to generate a complete, plausible motion trajectory. To ensure strict adherence to kinematics, we inject the joint priors at every layer of the DQ-VAE's non-autoregressive Transformer decoder using FiLM (Feature-wise Linear Modulation) conditioning. This persistent, multi-scale guidance is complemented by a numerically-stable cross-product loss to guarantee axis alignment. This decoupled design allows DragMesh to achieve real-time performance and enables plausible, generative articulation on novel objects without retraining, offering a practical step toward generative 3D intelligence. Code: https://github.com/AIGeeksGroup/DragMesh. Website: https://aigeeksgroup.github.io/DragMesh.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
