Lattice Boltzmann Model for Learning Real-World Pixel Dynamicity

2025-09-20

AI Summary

This work addresses the lack of physical interpretability and real-time capability in pixel-level dynamic modeling for visual tracking. We introduce the lattice Boltzmann method (LBM) into online point tracking for the first time, proposing a dynamic pixel lattice framework that explicitly models pixel motion states within spatiotemporal contexts via collision–streaming processes. A multi-layer prediction–update network is designed to enable physics-guided online representation optimization. The method supports efficient real-time inference and end-to-end training. It achieves significant improvements in accuracy and robustness on point-tracking benchmarks (TAP-Vid, RoboTAP) and open-world tracking tasks (TAO, BFT, OVT-B). Key contributions include: (i) the first LBM-based visual tracking paradigm; (ii) a physics-driven dynamic pixel modeling mechanism; and (iii) a lightweight online update architecture.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This work proposes the Lattice Boltzmann Model (LBM) to learn real-world pixel dynamicity for visual tracking. LBM decomposes visual representations into dynamic pixel lattices and solves pixel motion states through collision-streaming processes. Specifically, the high-dimensional distribution of the target pixels is acquired through a multilayer predict-update network to estimate the pixel positions and visibility. The predict stage formulates lattice collisions among the spatial neighborhood of target pixels and develops lattice streaming within the temporal visual context. The update stage rectifies the pixel distributions with online visual representations. Compared with existing methods, LBM demonstrates practical applicability in an online and real-time manner, which can efficiently adapt to real-world visual tracking tasks. Comprehensive evaluations of real-world point tracking benchmarks such as TAP-Vid and RoboTAP validate LBM's efficiency. A general evaluation of large-scale open-world object tracking benchmarks such as TAO, BFT, and OVT-B further demonstrates LBM's real-world practicality.

Problem

• Learning real-world pixel dynamicity for visual tracking
• Estimating pixel positions and visibility through collision-streaming processes
• Adapting efficiently to online real-time visual tracking tasks

Innovation

• LBM decomposes visual representations into dynamic pixel lattices
• Solves pixel motion via collision-streaming processes in predict-update network
• Efficiently adapts to real-world tracking with online visual representations