Mechanics-inspired neural networks provide physically structured models of dynamical systems, but typically assume access to the coordinates in which the underlying dynamics are expressed. While recent generative approaches relax this assumption, they remain limited in their ability to jointly model different dynamical regimes and discover interpretable latent structure. We introduce Structured Phase Space GAN (SPS-GAN), a conditional generative framework that learns mechanical dynamics and discovers latent phase-space structure from Cartesian trajectories or video observations. We achieve this by combining adversarial generation with port-Hamiltonian latent dynamics and extending cyclic-coordinate regularization to account for dissipation and forcing. This formulation supports conservative, dissipative, and forced systems while encouraging compact latent representations from which active degrees of freedom can be identified. Experiments on simulated and real-world systems demonstrate improved trajectory accuracy and video generation quality over competitive baselines, while identifying active latent dimensions consistent with the expected effective degrees of freedom and recovering physically interpretable coordinates across observation modalities and dynamical regimes.
Hamiltonian neural networks provide a principled framework for learning conservative dynamics. A scalar Hamiltonian is parameterized by a neural network and the vector field is derived using Hamilton's equations. However, preserving Hamiltonian structure does not guarantee that the energy function itself is represented accurately. When modeled by a conventional multilayer perceptron, the Hamiltonian inherits the spectral bias of coordinate-based neural representations, favoring smooth over fine-scale structure. These fine-scale errors can be amplified by differentiation when computing the dynamics. To address this limitation, we introduce Local Multiscale Hamiltonian Neural Network (LM-HNN), which restructures the Hamiltonian according to its physical interaction structure. LM-HNN decomposes the energy into kinetic and potential components and represents them as sums of shared local functions over multiple interaction neighborhoods. This replaces a single high-dimensional approximation problem with lower-dimensional, reusable interaction laws, making fine-scale energy structure easier to resolve while preserving Hamiltonian dynamics. Across low-dimensional, interacting, and fast--slow systems, LM-HNN consistently achieves more accurate Hamiltonian derivatives and long-horizon rollouts than competing structure-preserving models, while substantially extending the effective spectral range of the learned energy. The same structured representation also generalizes more reliably to energy levels not observed during training. These results show that accuracy depends on the Hamiltonian representation as well as its structure, and that incorporating locality, decomposition, and shared interaction laws provides a simple and effective route to more accurate and generalizable Hamiltonian learning.
Yanchao Bi, Pengfei Chen, Yulong Yan, Qian Zhang, Biao Wang, Leida Li
With the popularization of livestreaming applications, automatic camera tuning has become indispensable for ordinary users to enjoy high Quality of Experience (QoE). However, the existing Image Quality Assessment (IQA) methods face two challenges in camera tuning. First, they struggle to identify subtle quality difference between images, which is common in camera tuning. Second, the ability to explain fine-grained differences and to provide suggestions for camera tuning are even more lacking. To address these challenges, we first construct FGLive-Comments, an instruction tuning dataset with rich annotations of both preferences for fine-grained image pairs and quality reasoning from global and local visual evidence, which supports selective adjustment recommendations of camera parameters. Based on FGLive-Comments, we develop Q-CamTuning, a multimodal large language model for livestreaming camera tuning. Its progressive perception-evidence-guidance framework learns quality-aware visual representations, enhances fine-grained reasoning with global-local evidence, and maps quality issues to camera-parameter adjustment directions. Extensive experimental results demonstrate that Q-CamTuning outperforms existing IQA models in both quality scoring and fine-grained image pair ranking. User study also validates its ability to recommend camera tuning suggestions.