Hugging Face Trending Papers
PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation
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arXiv Computer Vision
Oct 7
PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation
arXiv:2610.07609v1 Announce Type: new Abstract: Neural simulation of high-fidelity deformable bodies is a foundational challenge in computer graphics and physical AI. Long-horizon prediction for high...
arXiv Computer Vision
4d ago
Neural Voxel Dynamics: Learning Volumetric Feature Advection for 3D Physics in V-JEPA Latent Space
Neural Voxel Dynamics introduces a self‑supervised framework that learns 3D latent dynamics from monocular video by unprojecting V‑JEPAs features into a voxel grid using depth priors. It then applies Volumetric Feature Advection, an action‑conditioned transition model, to predict the evolution of these latent features directly in 3D, without relying on privileged physics‑engine states. The method is evaluated on synthetic and real datasets, demonstrating predictive fidelity, 3D geometric consistency, and physical plausibility via Physics‑IQ metrics.
arXiv Computer Vision
Sep 14
Physics-Aware Video Generation via Agentic Planning and Graph-Guided Optimization
PhysPlan is a training‑free guidance framework that enhances video diffusion models by incorporating physical awareness through agentic physics simulation. It uses a vision‑language model to generate a Chain‑of‑Visual‑Thought representation of kinematic trajectories and 3D depth, which then drives an object‑centric test‑time optimization that isolates kinematic changes and locks the passive environment. The framework also employs Kinetic Intensity Profiling to adapt hyperparameters to varying physical deformations, and demonstrates superior performance on PhyGenBench and Physics‑IQ benchmarks compared to existing VDM baselines.
arXiv AI
Jun 17
Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion
arXiv:2603. 03485v3 Announce Type: replace-cross Abstract: Recent video diffusion models have achieved impressive capabilities as large-scale generative world models.
arXiv Computer Vision
Sep 15
Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion
arXiv:2605.04412v3 Announce Type: replace Abstract: 3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects fro...
arXiv Computer Vision
Sep 23
Point Diffusion Mamba: Unified Diffusion-State-Space Modeling for Single-View 3D Reconstruction under Data Scarcity
Point Diffusion Mamba (PDM) is a new method that fuses diffusion models with state‑space modeling to perform single‑view 3D reconstruction when training data are scarce. It uses a lightweight reconstruction module for unordered point‑clouds, a Local Geometric Aggregation module combined with Mamba blocks to capture both global geometry and local detail, and a Hierarchical Feature Integration Network to merge high‑level semantic and local geometric features for each point. A Dynamic Weighted Sampling strategy further improves reconstruction quality by integrating generative priors, and experiments on ShapeNet and Pix3D show that PDM outperforms existing state‑of‑the‑art approaches.