DMAligner: Enhancing Image Alignment via Diffusion Model Based View Synthesis

Xinglong Luo; Ao Luo; Zhengning Wang; Yueqi Yang; Chaoyu Feng; Lei Lei; Bing Zeng; Shuaicheng Liu

cs.CVFeb 26, 2026

DMAligner: Enhancing Image Alignment via Diffusion Model Based View Synthesis

Authors: Xinglong Luo, Ao Luo, Zhengning Wang, Yueqi Yang, Chaoyu Feng, Lei Lei, Bing Zeng, Shuaicheng Liu

Organizations: University of Electronic Science and Technology of China · Qianyuan Laboratory, Hangzhou, China · Southwest Jiaotong University · Independent Researcher

Abstract

Image alignment is a fundamental task in computer vision with broad applications. Existing methods predominantly employ optical flow-based image warping. However, this technique is susceptible to common challenges such as occlusions and illumination variations, leading to degraded alignment visual quality and compromised accuracy in downstream tasks. In this paper, we present DMAligner, a diffusion-based framework for image alignment through alignment-oriented view synthesis. DMAligner is crafted to tackle the challenges in image alignment from a new perspective, employing a generation-based solution that showcases strong capabilities and avoids the problems associated with flow-based image warping. Specifically, we propose a Dynamics-aware Diffusion Training approach for learning conditional image generation, synthesizing a novel view for image alignment. This incorporates a Dynamics-aware Mask Producing (DMP) module to adaptively distinguish dynamic foreground regions from static backgrounds, enabling the diffusion model to more effectively handle challenges that classical methods struggle to solve. Furthermore, we develop the Dynamic Scene Image Alignment (DSIA) dataset using Blender, which includes 1,033 indoor and outdoor scenes with over 30K image pairs tailored for image alignment. Extensive experimental results demonstrate the superiority of the proposed approach on DSIA benchmarks, as well as on a series of widely-used video datasets for qualitative comparisons. Our code is available at https://github.com/boomluo02/DMAligner.

Figures & tables

Figure 1: (a) Conventional image alignment based on optical flow and image warping, resulting in ghosting artifacts and occlusion. (b) Our DMAligner directly generates the complete alignment image via diffusion-based view synthesis.

Figure 2: Overview of DSIA dataset generation. The ground truth image is rendered by setting the time to $t_{2}$ and the camera pose to $P_{1}$ , where the background statics mirror those in $I_{1}$ , and the dynamics are akin to $I_{2}$ , with slight visualization variances caused by pose changes. As a result, $I_{gt}$ can be regarded as the alignment-oriented view synthesis with reference from $I_{2}$ to $I_{1}$ ( i.e., ${I_{2}}^{\prime}$ for $I_{2}$ to $I_{1}$ alignment).

Figure 3: Overview of our DMAligner. Instead of using the discriminative learning paradigm for optical flow estimation and image warping, our framework employs a generative approach to achieve image alignment with a diffusion model. Dynamics-aware Mask Producing (DMP) module is crucial for providing dynamic information, essential for performing the Dynamics-aware Diffusion Training process in this task.

Figure 4: Qualitative comparisons between DPFlow [ 34 ] , COGS [ 14 ] , GenWarp [ 41 ] and our DMAligner on our DSIA dataset.

Method	Input Structure
DPFlow [ 34 ]	2 images ( $I_{1},I_{2}$ )
COGS [ 14 ]	2 images ( $I_{1},I_{2}$ ) +
COGS [ 14 ]	Depth ( $D_{1},D_{2}$ ) + Mask ( $M_{1},M_{2}$ )
AccidentalGS [ 33 ]	2 images ( $I_{1},I_{2}$ ) +
AccidentalGS [ 33 ]	Depth ( $D_{1},D_{2}$ ) + Camera Intrinsics ( $K$ )
GenWarp [ 41 ]	Single image ( $I_{2}$ ) + Camera Intrinsics ( $K$ ) +

Table 1: Comparison of network inputs among DPFlow [ 34 ] , COGS [ 14 ] , AccidentalGS [ 33 ] , GenWarp [ 41 ] and our DMAligner. Other optical flow networks (PWCNet [ 44 ] , RAFT [ 45 ] , FlowFormer++ [ 42 ] , FlowDiffuser [ 27 ] ) have the same input structure as DPFlow.

Methods based on Flow Warping
Methods	LcLf		LcSf		ScLf		ScSf		Avg.
Methods	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$
PWCNet [ 44 ]	16.53	0.74	16.40	0.73	20.53	0.74	21.01	0.76	18.62	0.74
RAFT [ 45 ]	18.22	0.74	18.34	0.74	22.46	0.77	22.95	0.76	20.49	0.75
FlowFormer++ [ 42 ]	21.02	0.75	20.97	0.76	22.84	0.77	23.94	0.79	22.19	0.77
FlowDiffuser [ 27 ]	20.18	0.75	20.33	0.75	23.39	0.78	23.91	0.79	21.95	0.77

Table 2: Quantitative comparison on our DSIA dataset across four subsets (LcLf, LcSf, ScLf, and ScSf) using PSNR and SSIM metrics, higher scores indicate better performance . The average (“Avg”) across all subsets summarizes overall results. † indicates that occlusion and ghost regions are excluded during metric computation, as determined by forward-backward consistency check [ 50 ] .

Figure 5: Qualitative comparisons between DPFlow [ 34 ] , GenWarp [ 41 ] and our DMAligner on Sintel dataset.

Figure 6: Qualitative comparisons between DPFlow [ 34 ] , GenWarp [ 41 ] and our DMAligner on the real-world DAVIS dataset.

Figure 7: Comparison of HDR results using flow-based method HDRFlow [ 49 ] , DPFlow [ 34 ] and our DMAligner. Our DMAligner significantly reduces ghosting and background distortions, especially around occluded regions and moving objects.

Methods based on Flow Warping
Methods	Sintel (Clean)		Sintel (Final)		DAVIS		Avg.
Methods	LPIPS $\downarrow$	DreamSim $\downarrow$	LPIPS $\downarrow$	DreamSim $\downarrow$	LPIPS $\downarrow$	DreamSim $\downarrow$	LPIPS $\downarrow$	DreamSim $\downarrow$
PWCNet [ 44 ]	0.251	0.146	0.177	0.109	0.217	0.102	0.215	0.119
RAFT [ 45 ]	0.245	0.141	0.171	0.102	0.219	0.104	0.212	0.116
FlowFormer++ [ 42 ]	0.264	0.150	0.184	0.113	0.224	0.106	0.224	0.123
FlowDiffuser [ 27 ]	0.258	0.141	0.179	0.103	0.211	0.090	0.216	0.111

Table 3: Quantitative comparison on Sintel and DAVIS dataset. Due to the lack of ground truth for precise evaluation, we compute LPIPS and DreamSim [ 9 ] between

I_{pred}

and

I_{1}

. Lower scores indicate better performance .

Exp.	Predict	DMP	Avg
Exp.	Targets	DMP	PSNR	SSIM
(a)	pred $\hat{\epsilon}_{t}$	w/o	24.82	0.77
(b)	pred $x_{0}$	w/o	25.12	0.79
(c)	rectified flow	w/	25.98	0.80
(d)	v-prediction	w/	26.16	0.81
(e)	pred $x_{0}$	w/	26.67	0.81

Table 4: Ablation studies of predict targets and our DMP.

Figure 8: Stress test: performance vs. motion magnitude.

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Open paper AskPDF arXiv

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.

Abstract

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