InpaintSLat: Inpainting Structured 3D Latents via Initial Noise Optimization

Researchers present InpaintSLat, a training-free method for 3D inpainting that optimizes initial noise in structured 3D latent diffusion models. The approach leverages backpropagation approximation and spectral parameterization to improve geometric stability and contextual consistency, outperforming existing training-free baselines without requiring model retraining.

InpaintSLat addresses a fundamental challenge in generative 3D modeling: maintaining geometric coherence during inpainting operations. The research identifies that 3D geometric structures form early in the diffusion process and remain highly sensitive to initial noise conditions, creating instability when synthesizing new content while preserving existing context. This observation unlocks a novel optimization dimension previously underexplored in the field.

The technique builds on recent advances in structured 3D latent diffusion and rectified flow models, establishing a mathematical bridge between noise optimization and geometric fidelity. By applying spectral parameterization—a specialized representation for efficient optimization—the authors enable backpropagation-based refinement of initial noise without fine-tuning the underlying diffusion model. This training-free approach significantly reduces computational overhead compared to model retraining strategies.

The implications extend across 3D content creation, digital asset generation, and computer vision applications. For developers building 3D generative systems, this work demonstrates that sampling trajectory manipulation alone cannot fully solve inpainting stability; initial conditions warrant equal attention. The orthogonal contribution suggests opportunities for combining this approach with existing sampling techniques to achieve superior results.

Looking forward, this research likely influences how generative AI systems tackle structured data inpainting tasks. The success of initial noise optimization as an independent control dimension may inspire similar approaches in other diffusion-based frameworks. Future work should explore whether these principles transfer to other modalities or whether domain-specific optimizations yield further improvements.

• →Initial noise optimization establishes a novel, training-free control dimension for 3D inpainting orthogonal to existing sampling methods.
• →Spectral parameterization enables efficient backpropagation-based noise refinement without requiring model retraining.
• →Early-stage geometric structure formation in diffusion models exhibits high noise sensitivity, critical for inpainting stability.
• →The method demonstrates consistent improvements in both contextual consistency and prompt alignment over baseline approaches.
• →This approach directly addresses geometric instability in 3D generation tasks, a persistent challenge in structured latent diffusion.