Real-time texture transfer expands the creative horizon for interactive applications, enabling seamless detail projection in scenarios that range from digital character cosmetics to procedural automotive texturing. Yet, its practical application is governed by inherent trade-offs between processing speed and suppression of artifacts.
arXiv:2608. 13255v1 Announce Type: cross Abstract: Geometry-conditioned multi-view diffusion enables high-quality 3D texture generation, but its repeated per-view denoiser evaluations introduce substantial computational cost.
MeshSuite is the first benchmark designed to systematically evaluate triangle- and mesh-based neural rendering methods in both standard and dedicated engine deployment settings. It compares how different methods perform when using a conventional opaque mesh pipeline versus method-specific engine implementations, and also audits exported surfaces for topological and geometric integrity. The benchmark reveals that engine deployment generally degrades image quality, with MeshSplatting performing best under standard deployment, while dedicated deployment preserves fidelity at a higher computational cost.
T$^2$-Splat is a novel triangle‑based splatting method that separates topology adaptation from appearance refinement. It starts with Safe Restricted Delaunay Triangulation to set initial mesh connectivity, then uses Dynamic Topology Evolution to adjust triangle allocation via edge collapse and vertex split operations. Finally, residual per‑face Mip‑textures refine appearance on the fixed mesh with scale‑adaptive filtering, yielding competitive results on datasets such as Mip‑NeRF 360, Tanks & Temples, and DTU while allowing flexible mesh compression. "whyItMatters":"The approach achieves higher PSNR (0.71 dB over MeshSplatting on Mip‑NeRF 360) and supports extreme mesh compression, enabling efficient novel view synthesis with compact meshes."
GraftSR is a diffusion-based super‑resolution framework that uses reference images of the same object to guide texture restoration, mitigating hallucination. It introduces a dual‑mask reference guidance mechanism to decouple texture extraction from application, avoiding reliance on spatial alignment. The authors also release TexRefSR‑141K, a large dataset of reference pairs with spatial masks, and show that GraftSR outperforms existing methods on the TexRefSR‑Eval benchmark, reducing LPIPS by 20.2%.
arXiv:2607.13808v2 Announce Type: replace Abstract: Neural radiance representations in Gaussian Splatting (GS) deliver high-fidelity color detail but impose substantial rendering overhead from networ...
Splatting-based algorithms reconstruct photorealistic, real-time-renderable, and mesh-exportable 3D scenes from regular images, but they represent a scene as a single monolithic field. Therefore, the...
arXiv:2608.20687v1 Announce Type: new Abstract: 3D Gaussian Splatting has achieved remarkable success in novel view synthesis. However, extracting high-fidelity surfaces directly from 3DGS remains ch...
arXiv:2608.30423v1 Announce Type: cross Abstract: Splatting-based algorithms reconstruct photorealistic, real-time-renderable, and mesh-exportable 3D scenes from regular images, but they represent a...
NOFACE3D is a training‑free framework that refines fine‑scale, detail‑critical regions in 3D assets generated from a single image. It extracts a selected submesh, scales it to the generator’s input volume, and re‑encodes it into a full‑resolution latent grid, refining the region with flow‑based sampling guided by a spatially varying noise schedule. The refined region is then reintegrated, preserving surrounding geometry and appearance, and the method is evaluated on PAniC‑3D, TexVerse, and a new BigDetail benchmark, showing sharper geometry and finer texture detail. whyItMatters: The approach improves the fidelity of small, high‑curvature structures in image‑to‑3D models without requiring additional training, addressing a key limitation in current generators.
arXiv:2609.23380v1 Announce Type: new Abstract: Gaussian Splatting has enabled real-time novel view synthesis, but its tightly coupled geometry and appearance representation often require a large num...
arXiv:2607. 22890v1 Announce Type: cross Abstract: Domain Randomization (DR) is a standard technique for closing the Sim-to-Real gap, yet traditional DR pipelines rely on classical computer graphics rendering driven by polygon meshes.
Numerous 3D assets are discarded due to low texture resolution, while current super-resolution models ignore texture maps and focus on natural images. An efficient and generalizable texture super-resolution model can revitalize a large corpus of aging yet valuable assets across industries such as film and video games.