H-2DGS addresses the need for sharp, photorealistic neurosurgical surface reconstruction that can fuse with MRI and tractography without obscuring internal brain structures. The authors propose a three-stage pipeline that bootstraps appearance with conventional 3D Gaussian Splatting, contracts volumetric density along camera rays using differentiable quantiles, and converts the fused TSDF surface into curved height-field Gaussian patches. By explicitly representing a thin, deformable cortical sheet rather than a diffuse volume, the method targets geometric precision and compactness at the cost of some rendering fidelity.

Fine-tuning diffusion models has yielded over 100K community adapters, but merging them interactively creates a high-dimensional, expensive black-box optimization problem: prior work requires live image generation in every round and is limited to a few dozen preselected adapters. HimmBO splits the search into a screening stage that recovers promising adapters via level-set estimation over a precomputed gallery, followed by a refinement stage that optimizes merge coefficients only within that low-dimensional active set. A style-aware Gaussian-process kernel transfers preference information between visually similar adapters, allowing the system to scale to libraries of over one thousand adapters while using under one tenth the image generations of prior work.

Cameras and foundation models recognize what a 3D scene contains, but not what it is made of: surfaces of the same colour can be different materials. Spectral World Renderer tackles this by unifying world generation and hyperspectral unmixing inside 3D Gaussian splatting, so that each Gaussian carries material abundances decoded before ray compositing. The resulting SWR-Bench provides eight synthetic worlds with exact per-pixel material labels, enabling verifiable decomposition benchmarks and real-time material queries.

GeoFusion-CAD tackles the scalability bottleneck in parametric CAD generation, where Transformer-based methods struggle with long command sequences due to quadratic attention costs. The authors propose an end-to-end diffusion framework that encodes CAD programs as hierarchical trees and processes them with G-Mamba blocks—geometry-conditioned state-space models that achieve linear complexity $\mathcal{O}(Ld)$ while capturing geometric and topological dependencies. This enables scaling to sequences of up to 240 commands while maintaining high geometric fidelity.

Articulated object reconstruction typically requires either multi-view capture of discrete states or monocular video with a strict static-base-part assumption, limiting practical deployment. FreeArtGS introduces a "free-moving" setting where both joint angles and object poses vary arbitrarily during capture, using only a monocular RGB-D video. The method combines motion-based part segmentation via point tracking priors with joint estimation and 3D Gaussian Splatting optimization to jointly reconstruct geometry, appearance, and articulation.

Traditional latent diffusion models require staging—first train a VAE tokenizer, freeze it, then train a diffusion model on top. UNITE proposes a single-stage approach where a shared "Generative Encoder" serves as both tokenizer and denoiser via weight sharing, achieving FID 1.73 on ImageNet 256×256 without adversarial losses or pretrained encoders like DINOv2.

This paper tackles the domain generalization problem in image deraining, where models trained on synthetic data fail catastrophically on out-of-distribution (OOD) real-world scenarios. The authors propose a three-stage pipeline—Superpixel Generation, Resolution-adaptive Fusion, and Pseudo-label Re-Synthesis—that adapts source-domain models to target domains using only unpaired rain-free images, eliminating the need for costly paired rainy data collection.