FlexPainter is a new texture generation pipeline that offers flexible multi‑modal conditional guidance and produces highly consistent textures across multiple views. It builds a shared conditional embedding space to aggregate different input modalities and introduces an image‑based CFG method to separate structural and style information for reference image‑based stylization. The system generates multi‑view images simultaneously using a grid representation, applies view synchronization and adaptive weighting during diffusion sampling for local consistency, and finally uses a 3D‑aware texture completion model with a texture enhancement model to produce seamless, high‑resolution texture maps, outperforming state‑of‑the‑art methods in flexibility and quality.
arXiv:2608.25461v1 Announce Type: cross Abstract: Using conditional image generators, texture artists can explore many single-view looks for an existing 3D shape. Despite impressive progress, state-o...
Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challenging, especially when the input image contains rich...
arXiv:2605.04412v3 Announce Type: replace Abstract: 3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects fro...
arXiv:2609.39709v1 Announce Type: new Abstract: Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challengi...
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.
Image outpainting extends an image beyond its original borders, requiring seamless style integration and globally coherent scene completion. Building on the success of diffusion models, recent methods have achieved substantial improvements in visual quality.
The paper introduces GaussianBlender, a feed‑forward framework that enables instant, high‑fidelity, geometry‑preserving, and multi‑view consistent 3D stylization driven by text. It learns disentangled latent spaces for geometry and appearance using spatially‑grouped 3D Gaussians, and applies a latent diffusion model for text‑conditioned edits. Evaluations demonstrate that GaussianBlender outperforms methods requiring per‑instance optimization, making scalable 3D stylization practical for large‑scale production.
ArtNVG is a 3D stylization framework that uses reference style images to produce stylized 3D scenes with high reconstruction quality. Built on 3D Gaussian Splatting, it introduces Content‑Style Separated Control and Attention‑based Neighboring‑View Alignment to maintain local color and texture consistency across views. Experiments show that ArtNVG outperforms existing methods in content preservation, style alignment, and local consistency.
Despite progress in instruction-based video editing, unimodal textual instructions inherently struggle to convey fine-grained textures and complex dynamics. To bridge this perceptual gap, we propose Visual In-context Editing, a new paradigm elevating video editing from textual instructions to multi-modal visual guidance encompassing single image, image pair, and video pair.
arXiv:2511.22228v3 Announce Type: replace-cross Abstract: Recent advancements in diffusion and flow models have greatly improved text-based image editing, yet methods that edit images independently o...
arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.
arXiv:2606. 07117v1 Announce Type: cross Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations.