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arxivcs.CVcs.AIcs.GR2026-06-28

DR-GS: Physically-Based Deformable and Relightable 2D Gaussians

Jiaxin Li, Tong Wu, Yi Wei, Tailin Wu, Li Zhang

Gaussian splatting (GS) has garnered significant attention in VR/AR and digital content creation due to its explicit parameterization and efficient rendering capabilities. However, existing GS-based methods for deformable objects face two key limitations: (i) illumination is erroneously baked into textures, causing physically inconsistent responses under dynamic deformations and lighting changes; (ii) snapshot-based reconstruction restricts post-reconstruction material editing. To address these challenges, we propose Deformable and Relightable GS (DR-GS), a unified Gaussian framework that integrates physically-based inverse rendering, relighting, and deformation-aware manipulation. Through explicitly disentangling geometry, illumination, and material representations, DR-GS overcomes the limitations of static snapshots, resolving unrealistic appearance under varying conditions while enabling post-reconstruction parameter editing. Extensive experiments show that DR-GS achieves leading visual quality across static reconstruction, dynamic deformation, and relighting, reliably preserving reflections and specular highlights on glossy surfaces. It further establishes a fully decoupled geometry-illumination-material pipeline, enabling high-quality 3D asset creation and comprehensive post-editing.

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arxivcs.CVcs.AIcs.GR2026-06-28

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arxivcs.CVcs.AIcs.GRcs.LG2026-06-28

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arxivcs.CVcs.AIcs.GRcs.LGcs.RO2026-07-07

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arxivcs.AIcs.CVcs.GRcs.MAcs.MM2026-07-20

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arxivcs.GRcs.AIcs.CV2026-07-15

Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

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3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent years, neural simulation has become predominant, with methods such as NuRec playing a central role; how…

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