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Variational free complement method with Gaussian-expanded complement functions: convergence with fixed Gaussian expansion length
Physics > Chemical Physics [Submitted on 1 Jun 2026] Title:Variational free complement method with Gaussian-expanded complement functions: convergence with fixed Gaussian expansion length View PDF HTML (experimental)Abstract:For the free complement theory with Gaussian-expanded complement functions, the energy convergence of $n_\mathrm{G} = \mathrm{constant} < \infty, n\rightarrow\infty$ is discussed, where $n_\mathrm{G}$ is the number of the Gaussian functions in the STO-$n$G expansion.
Geometry Gaussians: Decoupling Appearance and Geometry in Gaussian Splatting
Announce Type: new Abstract: After the success of 3D Gaussian Splatting (3DGS) for novel view synthesis, many works have explored how to also use it for geometric surface representation. However, extracting accurate geometric information directly from 3DGS remains challenging and can often reduce the appearance rendering quality. In this work, we show that 3DGS in its default form is inheritedly unsuited to represent texture and geometry at the same time, by training with complete...
Gaussian Point Splatting
Gaussian Point Splatting Joris Rijsdijk, Christoph Peters, Michael Weinnman, Ricardo Marroquim. 2026–07 in ACM Transactions on Graphics (Proc. Official version Abstract We propose Gaussian point splatting, a stochastic method to render Gaussian splats that scales extremely well to scenes with many Gaussians.
Self-Learning Expression Deformations for Data-Efficient Gaussian Avatars
arXiv:2606.05912v1 Announce Type: new Abstract: Modeling dynamic facial expressions using 3D Gaussian representations remains challenging due to their unstructured nature. Conventional Gaussian avatar pipelines require extensive multiview and sequential expression data, limiting scalability and accessibility. In this work, we introduce Self-Adaptive Gaussian Expression (SAGE), a framework for self-learning expression-induced Gaussian deformations that enables high-fidelity, animatable...
Functional uniqueness and stability of Gaussian priors in optimal L1 estimation
arXiv:2511.16864v2 Announce Type: replace Abstract: We study when optimal Bayesian estimators under Gaussian noise are approximately linear, and what this implies about the underlying prior distribution. Consider the classical model \(Y = X + Z\), where \(Z\) is Gaussian and independent of \(X\). It is well known that under squared-error loss, the conditional mean \(\mathbb{E}[X|Y]\) is a linear function of \(Y\) if and only if the prior is Gaussian.
Gaussian Width of Convex Sets via Integral Decompositions, Projections, and the Distribution of Intrinsic Volumes
arXiv:2603.02714v2 Announce Type: replace-cross Abstract: We revisit the problem of bounding the expected supremum of a canonical Gaussian process indexed by a convex set $T \subset \mathbf{R}^d$. We develop two decompositions for the Gaussian width, based on the geometry of the index set. The first decomposition involves metric projections of Gaussians onto rescaled copies of $T$. The second involves fixed points arising from a quadratically penalized variant of the local width. Neither...
Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
arXiv:2604.03146v2 Announce Type: replace-cross Abstract: We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussian Min-Max Theorem (CGMT) to non-Gaussian settings, we derive an asymptotic min-max characterization of key statistics, enabling approximation of the mean $\mu_{\hat{\theta}}$ and covariance $C_{\hat{\theta}}$ of the ERM estimator $\hat{\theta}$. Specifically, under a concentration...
ZipSplat: Fewer Gaussians, Better Splats
arXiv:2606.05102v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting methods reconstruct a scene from posed or pose-free images in a single forward pass, yet current approaches predict one Gaussian per input pixel, tying the representation budget to camera resolution rather than scene complexity. A flat wall and a richly textured object thus produce equally many Gaussians despite very different geometric needs. We propose ZipSplat, a token-based feed-forward model that...
Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction
arXiv:2605.31595v1 Announce Type: new Abstract: Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame, suffering from duplicated Gaussians and view-dependent biases that hinder effective learning of scene motion. We present C4G, a feed-forward 4D reconstruction framework built upon a compact set of timestamp-conditioned learnable Gaussian query tokens.
$\text{VG}^2$GT: Voxel-Gaussian Splatting Visual Geometry Grounded Transformer
Announce Type: new Abstract: Gaussian splatting has shown strong potential for 3D reconstruction and novel view synthesis. However, most existing methods require accurate camera parameters and per-scene optimization, while feed-forward methods with pixel-aligned Gaussian primitives often suffer from artifacts and non-uniform primitives. In this paper, we propose $\text{VG}^2$GT, a Voxel-Gaussian Splatting Visual Geometry-Grounded Transformer.