7 accepted papers
Existing feed-forward networks excel at predicting a single set of physical properties from visual appearance, but this point-estimate paradigm fundamentally fails to capture the real world's inherent physical ambiguity. We address this by reframing physics prediction as a task of learning a control
Indoor positioning with 5G technologies depends on accurate Channel State Information (CSI) for high-precision location services, but limited data, especially in low signal-to-noise ratio (SNR) environments, presents a significant challenge. Traditional deep learning methods, relying on "Learning +…
Remote Sensing Change Detection (RSCD) is essential for identifying alterations within geographical landscapes based on dual-temporal imagery. Current methods often enhance global modeling through the use of Transformers or integrate global and local features in a coarse-grained manner. The former t…
Cloud detection (CD) in remote sensing images is commonly used in satellite imaging and laser communication. UNet-based methods with multi-level feature caching and interaction learning, are popular for superior CD performance. However, most current CD methods focus on spatial feature enhancement th…
Fifth-generation communication technology enables advanced indoor positioning with its high bandwidth and frequency capabilities. However, indoor environment variability causes signal propagation fluctuations, making existing models inadequate for accurate location estimation. In this paper, we demo…
Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limitations, we introduce PhysCtrl, a novel framework for physics-grounded image-to-video generation with physical parameters…
Faithfully reconstructing textured shapes and physical properties from videos presents an intriguing yet challenging problem. Significant efforts have been dedicated to advancing such a system identification problem in this area. Previous methods often rely on heavy optimization pipelines with a dif…