This work introduces an exact analytical pixel-to-texel mapping that aligns diffusion trajectories across multiple viewpoints, and utilizes High-Resolution Latent Textures as a persistent canvas for gradually denoised textures, while camera views perform the denoising steps in latent pixel space.
Abstract
SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first to demonstrate that high-resolution textures, previously limited to 2D synthesis, can be generated directly on 3D objects without model fine-tuning or optimization. Designed for complex, multi-object environments, SceneHI uniquely combines 3D-consistency, high-resolution fidelity, and physically plausible baked shadows within a single generative pipeline. To enforce strict geometric coherence, we introduce an exact analytical pixel-to-texel mapping that aligns diffusion trajectories across multiple viewpoints. We utilize High-Resolution Latent Textures (HRLTs) as a persistent canvas for gradually denoised textures, while camera views perform the denoising steps in latent pixel space. This ensures a shared base texture that can be subsequently refined to high resolution without compromising multi-view consistency. Finally, a light-aware generative pass embeds realistic geometry-consistent shadows directly into the atlases, bridging the gap to production workflows. SceneHI achieves high visual fidelity while reducing generation time by 80% compared to existing scene-level methods.
This work presents FixAnything, a single model for fixing a wide range of rendering artifacts by repurposing a pretrained video generative model, leveraging its implicit multi-view priors with only minimal modification and lightweight finetuning.
Khiem Vuong, D. Ramanan, Srinivasa G. Narasimhan· 1 citation
LumiTokens is a framework that formulates 3D relighting as a direct transformation on latent scene tokens, without explicit 3D representations, rendering equations, or physics-based decomposition, and achieves comparable or superior relighting quality to other methods and supports progressive, composable lighting edits...
Automated generation of production-ready 3D garment assets from a single image is a central challenge in digital content creation. While recent generative models have significantly advanced 3D geometry reconstruction, synthesizing high-quality textures remains a bottleneck. Existing methods often bake environmental ill...
Di Huang, Yuan-Hao Wang, Cheng Zhang et al.· 0 citations
PoseAdapter, a lightweight framework for high-fidelity 2.5D controllable image generation, and a Context-Aware Dual-Stream Representation, to resolve the generative trade-off between strict instance isolation and global coherence.
Yu-Feng Chi, Hui-Min Ma, Fan Gao et al.· 0 citations
CasDeblurGS is proposed, a cascaded framework that progressively recovers reliable cross-view information from local 2D correspondences to global 3D guidance that enables a more coherent 3D representation and higher-quality novel-view synthesis.
This work proposes a novel 3D-aware video restoration framework designed to enhance the quality of sparse 3DGS reconstruction and introduces a camera-conditioned geometric prior that guides the network toward geometrically grounded restoration that remains coherent across viewpoints.
Xinhui Liu, Can Wang, Wei Jiang et al.· 1 citation