5 accepted papers
We present \textbf{Frequency-Aware Dynamic Gaussian Splatting (FAGS)}, a novel approach to mitigating motion blur in 4D reconstruction, particularly under novel viewpoints. This blur stems from a fundamental spectral conflict in existing methods, which struggle to \textbf{balance high-frequency ren…
Constructing a 4D language field that supports open-vocabulary queries is essential for semantic perception and interaction in dynamic environments. Existing 4D Gaussian-based approaches face two major challenges. First, the assumption of a static identity per Gaussian leads to semantic inconsistenc
The ability to extrapolate dynamic 3D scenes beyond the observed timeframe is fundamental to advancing physical world understanding and predictive modeling. Existing dynamic 3D reconstruction methods have achieved high-fidelity rendering of temporal interpolation, but typically lack physical consist
While deep neural networks have made remarkable progress in various tasks, their performance typically deteriorates and faces insecurity when tested in out-of-distribution (OOD) scenarios. Many OOD methods focus on extracting domain-invariant features but neglect whether these features are unique to…
In contemporary machine learning, enhancing model generalization through diversified datasets is essential. Yet, collecting additional data often faces prohibitive costs and privacy constraints, with no guarantee of improved diversity. In this paper, we propose Domain-Diff, featuring a pivotal Word-…