NeuROK: Generative 4D Neural Object Kinematics

Problem
This paper addresses the gap in generating realistic 4D dynamics of static objects, which is crucial for comprehensive 3D world modeling. Existing methods typically rely on predefined physical models and system identification, limiting their applicability to specific object categories and small datasets. The authors propose a novel approach, NeuROK, to overcome these limitations by learning a data-driven kinematic state parameterization for object-centric physical systems. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the introduction of Neural Object Kinematics (NeuROK), which employs a transformer-based encoder-decoder architecture. The model learns a latent space that encapsulates all possible states of an object, allowing for the generation of plausible deformations. The encoder processes input data to create a low-dimensional representation, while the decoder maps sampled latent vectors to deformed object shapes. The training is conducted on a curated large-scale 4D dataset, although specific details regarding the dataset size, training compute, and loss functions are not disclosed. The approach leverages principles from Lagrangian mechanics, simplifying the dynamics generation process by focusing on a low-dimensional latent space.

Results
NeuROK demonstrates significant improvements over existing baselines in generating realistic 4D dynamics. The authors report that their model outperforms prior works across various dynamic object types, although specific quantitative metrics (e.g., FID scores, accuracy percentages) and named baselines are not detailed in the abstract. The results indicate that NeuROK can effectively simulate complex deformations under diverse physical conditions, showcasing its generality and effectiveness in comparison to traditional methods.

Limitations
The authors acknowledge that their approach may be constrained by the quality and diversity of the training dataset, which could affect the generalization of the learned kinematic representations. Additionally, the reliance on a transformer architecture may introduce computational overhead, particularly for real-time applications. The paper does not address potential challenges related to the interpretability of the learned latent space or the scalability of the model to more complex physical interactions beyond those represented in the training data.

Why it matters
The implications of NeuROK are significant for downstream applications in robotics, virtual reality, and computer graphics, where realistic object interactions and dynamics are essential. By providing a framework that simplifies the generation of 4D dynamics, this work opens avenues for more sophisticated simulations in environments that require dynamic object manipulation. Furthermore, the data-driven approach may inspire future research into unsupervised learning of physical models, potentially leading to advancements in autonomous systems that can better understand and interact with their environments.

Authors: Chen Geng, Guangzhao He, Yue Gao, Yunzhi Zhang, Shangzhe Wu, Jiajun Wu
Source: arXiv:2605.30347
URL: https://arxiv.org/abs/2605.30347v1