AlignMorph: Tuning-Free Diffusion Image Morphing via Explicit Semantic Transport

2026-09-21

AI Summary

为解决图像变形中的布局差异问题，提出AlignMorph方法，通过显式语义传输和坐标对齐生成实现无需调整的平滑过渡。

Abstract

Image morphing aims to produce a smooth and semantically consistent transition between two input images. Existing diffusion-based morphing methods either require expensive per-pair optimization or rely on implicit spatial alignment, which easily fails under large layout discrepancies. To address these limitations, we propose AlignMorph, a novel tuning-free diffusion framework guided by the principle of transport-then-denoise. We explicitly decouple geometric alignment from generative denoising to avoid structural entanglement. Our framework consists of two core components. (1) Global Semantic Transport, which achieves diffusion-compatible semantic alignment via entropic optimal transport and reliability-aware latent warping; and (2) Coordinate-Aligned Generation, which uses a symmetric bi-phase attention handoff to maintain consistent spatial coordinates throughout denoising. Without any tuning, AlignMorph effectively eliminates ghosting and achieves superior structural coherence and temporal smoothness on morphing benchmarks. Code is available at https://github.com/51xOne/Alignmorph.

Problem

• Image Morphing
• Diffusion
• Semantic Alignment
• Layout Discrepancies
• Geometric Alignment

Innovation

• tuning-free diffusion
• semantic transport
• coordinate-aligned generation
• entropic optimal transport
• latent warping