arXiv Computer Vision By Wuyi Liu, Xu Han, Yuren Chen, Yige Mao, Zishuo Peng, Xianzhi Li

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

Read the original on arXiv Computer Vision →

AlignMorph is a tuning‑free diffusion framework for image morphing that separates geometric alignment from generative denoising. It uses Global Semantic Transport—entropic optimal transport and reliability‑aware latent warping—to achieve diffusion‑compatible semantic alignment, and Coordinate‑Aligned Generation—symmetric bi‑phase attention handoff—to preserve spatial coordinates during denoising. The method eliminates ghosting and delivers superior structural coherence and temporal smoothness on morphing benchmarks without any per‑pair optimization.

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arXiv AI
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arXiv:2606. 24874v1 Announce Type: cross Abstract: Sparse voxel representation has emerged as a scalable foundation for image-to-3D Gaussian Splatting (3DGS) generation, yet current methods struggle to preserve high-frequency visual details of input images due to two structural bottlenecks.

By Haorui Ji, Weizhe Liu, Hongdong Li, Hengkai Guo
arXiv AI
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By Han Lin, Xichen Pan, Zun Wang, Yue Zhang, Chu Wang, Jaemin Cho, Mohit Bansal