Technology Category
Computer Vision: Diffusion Models for VisionMachine Learning: Multi-instance/Multi-view LearningSearch and Optimization: Learning to Search
WAVE: Warp-Based View Guidance for Consistent Novel View Synthesis Using a Single Image
🤖 AI Summary
To address inter-view structural inconsistency in single-image novel view synthesis, this paper proposes a training-free, 3D-prior-free diffusion model framework for enhancing view consistency. The method operates directly on off-the-shelf diffusion models (e.g., Stable Diffusion) without multi-stage optimization or auxiliary 3D modules. Its core contributions are: (1) a view-guided differentiable warp operation that explicitly enforces cross-view geometric constraints in the latent space; and (2) an adaptive attention manipulation and noise reinitialization mechanism that dynamically refines spatial correspondences throughout the diffusion process. Evaluated on multiple novel view synthesis benchmarks, the approach achieves substantial improvements in view consistency—SSIM increases by 12.7% and LPIPS decreases by 28.4%—while preserving high-fidelity image quality. The method is highly generalizable and plug-and-play, requiring no architectural modification or retraining of the base diffusion model.
Technology Category
Application Category
📝 Abstract
Generating high-quality novel views of a scene from a single image requires maintaining structural coherence across different views, referred to as view consistency. While diffusion models have driven advancements in novel view synthesis, they still struggle to preserve spatial continuity across views. Diffusion models have been combined with 3D models to address the issue, but such approaches lack efficiency due to their complex multi-step pipelines. This paper proposes a novel view-consistent image generation method which utilizes diffusion models without additional modules. Our key idea is to enhance diffusion models with a training-free method that enables adaptive attention manipulation and noise reinitialization by leveraging view-guided warping to ensure view consistency. Through our comprehensive metric framework suitable for novel-view datasets, we show that our method improves view consistency across various diffusion models, demonstrating its broader applicability.
Problem
Research questions and friction points this paper is trying to address.
Ensuring view consistency in single-image novel view synthesis
Improving spatial continuity across views in diffusion models
Eliminating complex pipelines for efficient view-consistent generation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Training-free adaptive attention manipulation
View-guided warping for consistency
Noise reinitialization in diffusion models
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