# 神经物体运动学：NeuROK

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-28 08:00
- AIHOT 分数：63
- AIHOT 链接：https://aihot.virxact.com/items/cmpqd5x5k03y1slnol1nmrqk3
- 原文链接：https://arxiv.org/abs/2605.30347

## AI 摘要

当前数据驱动方法在静态3D物体重建上表现突出，但生成符合物理规律的4D动态形变仍具挑战，现有方法多依赖预定义物理模型与参数估计，局限于特定类别。该研究提出NeuROK，通过学习一个表征物体所有可能状态的潜空间及一个将潜空间采样映射为合理形变形状的解码器，实现了数据驱动的运动状态参数化。其在大规模4D数据集上训练了基于Transformer的编码器-解码器模型，将动态生成简化为低维潜空间中的操作，从而能更高效地生成多类物体的逼真动态。

## 正文

Data-driven approaches have revolutionized 3D vision, enabling transformers to effectively reconstruct and generate static 3D objects. However, generating simulative 4D dynamics -- realistic temporal deformations of static objects under various physical conditions -- remains challenging and often ad hoc, despite its importance in building comprehensive 3D world models. Most existing methods assume a predefined physical model and use system identification to estimate parameters, restricting these methods to specific categories and small-scale datasets. We propose that these restrictions can be overcome by learning a data-driven kinematic state parameterization for object-centric physical systems. Specifically, we learn both a latent space representing all possible states of the object and a decoder that maps any sampled latent to a plausibly deformed shape of the object. We refer to this parameterization as Neural Object Kinematics (NeuROK), and learn a transformer-based encoder-decoder model on a curated large-scale 4D dataset. This formulation and the learned model significantly simplify the generation of simulative dynamics since we only need to consider the dynamics within a low-dimensional latent space from the Lagrangian mechanics' perspective in classical physics. We demonstrate the effectiveness and generality of this neural simulation framework across diverse dynamic object types, showing clear advantages over prior works. Project page: https://chen-geng.com/neurok