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ECCV 2026

Large-Scale Light Field Synthesis from Videos Enables Geometrically Consistent Bokeh Editing

Haoming Cai, Zhoutong Zhang, Christopher Metzler, Shumian Xin

Focus and depth of field (DoF) define where attention falls and how much of a scene appears sharp. Adjusting both after capture provides creators two-dimensional control over visual attention. However, existing focus and DoF editing tools fail when applied to scenes with complex geometries, such as reflections and fine features. This failure is a result of fundamental limitations in existing training data generation pipelines: current pipelines either sacrifice geometric fidelity for scalability, or sacrifice scalability for geometric fidelity. To address this, we present Vid2Bokeh, a scalable bokeh data acquisition pipeline that reconstructs dense 25×25 light fields from casual videos via feed-forward 3D reconstruction, bypassing depth estimation entirely and avoiding the geometric errors it introduces. The resulting Vid2Bokeh Dataset comprises over 100K light fields on diverse real-world scenes, simultaneously achieving scene diversity, optical density, and geometric fidelity that no existing bokeh dataset or bokeh data acquisition pipeline provides. By training on this geometrically faithful bokeh data, we introduce a diffusion model for full bidirectional focus–DoF editing that outperforms depth-based baselines on complex geometries. This result demonstrates that geometric fidelity in large-scale training data is the key to geometrically consistent bokeh editing.

ECCV 2026

UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors

Xiaoyan Cong, Zekun Li, Zhiyang Dou, Hongyu Li, Omid Taheri, Chuan Guo, Abhay Mittal, Sizhe An, Taku Komura, Wojciech Matusik, Michael Black, Srinath Sridhar

Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets and paired text descriptions. However, how to effectively and efficiently leverage such single-purpose motion LFMs, i.e., text-to-motion synthesis, in more diverse cross-modal and in-context motion generation downstream tasks remains largely unclear. Prior work typically adapts pretrained generative priors to individual downstream tasks in a task-specific manner. In contrast, our goal is to unlock such priors to support a broad spectrum of downstream motion generation tasks within a single unified framework. To bridge this gap, we present UMO, a simple yet general unified formulation that casts diverse downstream tasks into compositions of atomic per-frame operations, enabling in-context adaptation to unlock the generative priors of pretrained DiT-based motion LFMs. Specifically, UMO introduces three learnable frame-level meta-operation embeddings to specify per-frame intent and employs lightweight temporal fusion to inject in-context cues into the pretrained backbone, with negligible runtime overhead compared to the base model. With this design, UMO finetunes the pretrained model, originally limited to text-tomotion generation, to support diverse previously unsupported tasks, including temporal inpainting, text-guided motion editing, text-serialized geometric constraints, and multi-identity reaction generation. Experiments demonstrate that UMO consistently outperforms task-specific and training-free baselines across a wide range of benchmarks, despite using a single unified model. Code and models will be publicly available.

ECCV 2026

TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction

Ao Li, Yonggen Ling, Yiyang Lin, Yuji Wang, Yong Deng, Yansong Tang

Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventional 3D human keypoints estimation methods primarily focus on the whole-body reconstruction quality relative to the root joint. However, in practical human-robot interaction (HRI) scenarios, robots are more concerned with the precise metric-scale spatial localization of task-relevant body parts under the egocentric camera 3D coordinate. We propose TAIHRI, the first Vision-Language Model (VLM) tailored for close-range HRI perception, capable of understanding users’ motion commands and directing the robot’s attention to the most taskrelevant keypoints. By quantizing 3D keypoints into a finite interaction space, TAIHRI precisely localize the 3D spatial coordinates of critical body parts by 2D keypoint reasoning via next token prediction, and seamlessly adapt to downstream tasks such as natural language control or global space human mesh recovery. Experiments on egocentric interaction benchmarks demonstrate that TAIHRI achieves superior estimation accuracy for task-critical body parts. We believe TAIHRI opens new research avenues in the field of embodied human-robot interaction. Code is available at: https://github.com/Tencent/TAIHRI.

ECCV 2026

MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE

Junzhe Li, Yutao Cui, Tao Huang, Chuxuan Zeng, Weijie Kong, Yinping Ma, Chun Fan, Miles Yang, Zhao Zhong, Liefeng Bo

Although GRPO substantially improves flow-matching models for human preference alignment in vision generation, mainstream methods such as DanceGRPO rely on Global-Stochastic Di!erential Equations (SDE) sampling across full timesteps in the Markov Decision Process (MDP), which remains computationally ine"cient. In this paper, we propose MixGRPO, a novel framework that leverages the flexibility of mixed sampling strategies through the integration of SDE and Ordinary Di!erential Equations (ODE). This redesign streamlines optimization within the MDP, delivering gains in both generation performance and training e!ciency. Specifically, MixGRPO introduces a sliding window mechanism, using SDE sampling and GRPO-guided optimization only within the window, while applying ODE sampling outside. This design confines sampling randomness to the time-steps within the window, thereby reducing the optimization overhead, and allowing for more focused gradient updates to accelerate convergence. Additionally, as time-steps beyond the sliding window are not involved in optimization, higher-order solvers are supported for faster sampling. So we present a faster variant, termed MixGRPO-Flash, which further improves training e"ciency while achieving comparable performance. MixGRPO exhibits substantial gains across multiple dimensions of human preference alignment, outperforming DanceGRPO in both e!ectiveness and e"- ciency, with nearly 50% lower training time. Notably, MixGRPOFlash further reduces training time by 71%.5

ECCV 2026

ConsiSpace: Learning Geometric Consistency Matters for Video Spatial Reasoning

Ting Huang, Zhenyu Zhang, Wenyuan Huang, Hao Tang, Jian Yang

Video spatial reasoning is essential for navigation-oriented perception and long-video question answering, where models must infer spatial relations across long horizons under changing viewpoints. However, existing multimodal large language models (MLLMs) remain largely semantic-centric, and often fail to reliably aggregate consistent spatial evidence from redundant video observations, leading to inefficient or unstable reasoning. To address these issues, we propose ConsiSpace, a geometry-consistency-aware framework for geometry-sensitive video spatial reasoning that turns spatial consistency into both an evidence organization principle and an explicit post-SFT learning signal. We build a geometry-consistent memory (GCM) including implicit evidence tokens and explicit geometric cues, and leverage efficient organization strategies to compactly preserve task-related spatial evidence. Furthermore, we utilize unified consistency self-supervised reinforcement learning (UCSSRL) after supervised fine-tuning to improve cross-view stability, with answer-, metric-, and topology-consistency rewards. Extensive experiments on three spatial-reasoning benchmarks, VSI-Bench, OSI-Bench, and MMSI-Video-Bench, show consistent gains, improving the average score by 12.6 points over the strongest baselines.

ECCV 2026

FED-Bench: A Cross-Granular Benchmark for Disentangled Evaluation of Facial Expression Editing

Fengjian Xue, Xuecheng Wu, Heli Sun, Yunyun Shi, Shi Chen, Liangyu Fu, Jinheng Xie, Dingkang Yang, Hao Wang, Junxiao Xue, Liang He

Facial expression image editing requires fine-grained control to strictly preserve human identity and background while precisely manipulating expression. However, existing editing benchmarks primarily focus on general scenarios, lacking high-quality facial images and corresponding editing instructions. Furthermore, current evaluation metrics exhibit systemic biases in this task, often favoring lazy editing or overfit editing. To bridge these gaps, we propose FED-Bench, a comprehensive benchmark featuring rigorous testing and an accurate evaluation suite. First, we carefully construct a benchmark of 747 triplets through a cascaded and scalable pipeline, each comprising an original image, an editing instruction, and a ground-truth image for precise evaluation. Second, we introduce FED-Score, a cross-granularity evaluation protocol that disentangles assessment into three dimensions: Alignment for verifying instruction following, Fidelity for testing image quality and identity preservation, and Relative Expression Gain for quantifying the magnitude of expression changes, effectively mitigating the aforementioned evaluation biases. Third, we benchmark 18 image editing models, revealing that current approaches struggle to simultaneously achieve high fidelity and accurate expression manipulation, with fine-grained instruction following identified as the primary bottleneck. Finally, leveraging the scalable characteristic of introduced benchmark engine, we provide a 20k+ in-the-wild facial training set and demonstrate its effectiveness by fine-tuning a baseline model that achieves significant performance gains. Our benchmark and code are available at https://github.com/hiixfj/FED-Bench.

ECCV 2026

CS-TTA: Preserving Concept Sensitivity in Test-Time Adaptation

Junah Jung, YeonGyu Han, Chang Min Park, Dongheon Lee

Test-time adaptation (TTA) methods are typically evaluated by task accuracy, leaving open the question of whether adaptation also changes which features the model relies on. We examine this question by tracking concept sensitivity throughout adaptation using TCAV directional derivatives. Across five TTA methods and five benchmarks spanning natural image and medical imaging domains, we observe recurring shifts in concept sensitivity: adaptation can reduce sensitivity to task-relevant or pathology-related concepts while increasing sensitivity to nuisance concepts such as backgrounds or institutional artifacts—a phenomenon we term Concept Sensitivity Drift, even when accuracy improves. On Waterbirds, where the causal/spurious split is defined by construction, this drift coincides with degraded worst-group accuracy; on cross-hospital medical adaptation, it coincides with increased reliance on hospital-specific artifacts rather than pathology. Motivated by this observation, we propose CS-TTA, a source-concept-supervised but target-label-free plug-in regularizer that mitigates excessive conceptsensitivity drift during adaptation. CS-TTA requires no architectural changes and can be added to any existing TTA objective. Experiments show that CS-TTA provides consistent worst-group accuracy gains on spurious-correlation benchmarks, improves AUC by 1.4–1.8 points on cross-hospital medical adaptation, and gives consistent overall accuracy gains on CIFAR-10-C and ImageNet-C.1234

ECCV 2026

Matryoshka Gaussian Splatting

Zhilin Guo, Boqiao Zhang, Hakan Aktas, Kyle Fogarty, Jeffrey Hu, Nursena Aslan, Wenzhao Li, Canberk Baykal, Albert Miao, Josef Bengtson, Chenliang Zhou, Weihao Xia, Cristina Vasconcelos, Cengiz Oztireli

The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existing discrete LoD methods expose only a limited set of operating points, while concurrent continuous LoD approaches enable smoother scaling but often suffer noticeable quality degradation at full capacity, making LoD a costly design decision. We introduce Matryoshka Gaussian Splatting (MGS), a training framework that enables continuous LoD for standard 3DGS pipelines without sacrificing full-capacity rendering quality. MGS learns a single ordered set of Gaussians such that rendering any prefix, the first k splats, produces a coherent reconstruction whose fidelity improves smoothly with increasing budget. Our key idea is stochastic budget training: each iteration samples a random splat budget and optimises both the corresponding prefix and the full set. This strategy requires only two forward passes and introduces no architectural modifications. Experiments across four benchmarks and six baselines show that MGS matches the full-capacity performance of its backbone while enabling a continuous quality–speed trade-off from a single model. Extensive ablations on ordering strategies, training objectives, and model capacity further validate the designs.

ECCV 2026

BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing

Jiacheng Chen, Ramin Mehran, Xuhui Jia, Saining Xie, Sanghyun Woo

We present BlenderFusion, a generative visual compositing framework that recomposes objects, camera, and background to synthesize new scenes. It follows a layering-editing-compositing pipeline that (i) segments and lifts up visual inputs into editable 3D entities (layering), (ii) edits them in Blender with 3D-grounded control (editing), and (iii) fuses them into a coherent scene using a generative compositor (compositing). The generative compositor extends a pre-trained diffusion model to a novel dual-stream architecture, processing both the original (source) and edited (target) scenes in parallel, and is fine-tuned on video frames with two important training strategies: (i) source masking, enabling flexible image modifications like background replacement or object replacement; (ii) simulated object jittering, facilitating robust disentangled control over objects and the camera. Extensive experiments on synthetic and real-world datasets show that BlenderFusion significantly outperforms prior methods in precise 3D-aware control and complex compositional scene editing. The framework also generalizes to unseen data and fine-grained editing operations beyond the training distribution. See the project page for demos and more results: blenderfusion.github.io.

ECCV 2026

SA-V2V: Training-Free Subject-Aware Video-to-Video Personalization

Soobin Park, Seohyeon Yoo, Jiwon Kim, SeonHwa Kim, Kyong Hwan Jin, Eunju Cha

Video personalization aims to generate personalized videos by preserving the motion and background of a reference video while transferring the subject appearance from a given subject image. Such video personalization has achieved tremendous progress alongside recent advancements in video diffusion transformers (DiT). Existing DiT-based approaches predominantly rely on training-based methods that require auxiliary control signals, while independent control over motion, subject appearance, and background remains challenging due to the entangled nature of the unified spatiotemporal attention mechanism. In this paper, we uncover a key structural insight that the spatio-temporal attention matrix of DiT exhibits an inherent functional decomposition—intra-frame blocks primarily encode spatial information, while inter-frame blocks encode motion dynamics. Building on this observation, we introduce SA-V2V, a novel training-free video personalization framework that disentangles and enables independent control over these visual attributes. We propose two guidance approaches: (1) For motion guidance, we present stochastic temporal injection, which leverages inter-frame attention maps to precisely control dynamic trajectories.(2) For appearance guidance, we propose a Target-aware Feature Guidance to enable independent control over subject and background appearance by modulating the synthesized features with masked attention features from the subject image and the reference video, respectively. Extensive experiments demonstrate that the proposed SA-V2V significantly outperforms existing approaches in motion fidelity, subject adherence, and background preservation. Our code is available at https://parksoobin1219.github.io/SA-V2V/

ECCV 2026

Finding Highlight Images In Your Albums:From Benchmark To MLLM

Rong Qin, Congcong Sun, Yaopeng Dong, Yanbin Sun, Chensen Ding, Chenxi Zhao, Biao Wang, Qian Zhang, Eunil Park, Chi-Man Vong, Jufeng Yang

Album highlight recommendation (AHR) focuses on identifying images from personal collections that are most suitable for personal enjoyment and social sharing. However, due to the inherent subjectivity and ambiguity involved in selecting highlights, AHR research has long been hindered by limited data availability, resulting in relatively slow progress in the field. To mitigate this issue, we first engage multiple annotators to re-annotate the public PEC and CUFED album datasets, producing high-quality labels for 910 albums comprising 87K images. These binary annotations are then aggregated into finegrained highlight scores to reduce individual annotation bias. To assess how well public consensus aligns with personal preferences, we further collect 10K images from 100 volunteers and invite the album owners themselves to provide personalized highlight annotations. This newly constructed dataset is named C-AHR-100. In addition to contributing these three benchmark datasets, we propose Highlight4U, an agentic multi-modal large language model (MLLM) framework specifically optimized for AHR. Highlight4U first identifies highlight-worthy reference images through localized scoring of individual images, and then refines its recommendations by incorporating contextual and semantic information across the reference set. Extensive experiments demonstrate that Highlight4U effectively captures public highlight perception and can further generalize to accommodate the personalized preferences of individual album owners. The source code and dataset are provided in the https://github.com/naninasm/Finding-Highlight-Images-InYour-Albums-From-Benchmark-To-MLLM.

ECCV 2026

Anchor Forcing: Anchor Memory and Tri-Region RoPE for Interactive Streaming Video Diffusion

Yang Yang, Tianyi Zhang, Wei Huang, Jinwei Chen, Boxi Wu, Xiaofei He, Deng Cai, Bo Li, Peng-Tao Jiang

Interactive long video generation requires prompt switching to introduce new subjects or events, while maintaining perceptual fidelity and coherent motion over extended horizons. Recent distilled streaming video diffusion models reuse a rolling KV cache for long-range generation, enabling prompt-switch interaction through re-cache at each switch. However, existing streaming methods still exhibit progressive quality degradation and weakened motion dynamics. We identify two failure modes specific to interactive streaming generation: (i) at each prompt switch, current cache maintenance cannot simultaneously retain KV-based semantic context and recent latent cues, resulting in weak boundary conditioning and reduced perceptual quality; and (ii) during distillation, unbounded time indexing induces a positional distribution shift from the pretrained backbone’s bounded RoPE regime, weakening pretrained motion priors and long-horizon motion retention. To address these issues, we propose Anchor Forcing, a cache-centric framework with two designs. First, an anchor-guided re-cache mechanism stores KV states in anchor caches and warm-starts re-cache from these anchors at each prompt switch, reducing post-switch evidence loss and stabilizing perceptual quality. Second, a tri-region RoPE with region-specific reference origins, together with RoPE re-alignment distillation, reconciles unbounded streaming indices with the pretrained RoPE regime to better retain motion priors. Experiments on long videos show that our method improves perceptual quality and motion metrics over prior streaming baselines in interactive settings.

ECCV 2026

MAGiSt3R: Multi-Agent Feed-forward 3D Reconstruction from Monocular RGB Videos

Ziren Gong, Xiaohan Li, Fabio Tosi, Ninghui Xu, Stefano Mattoccia, Jianfei Cai, Matteo Poggi

This paper presents MAGiSt3R, a multi-agent 3D reconstruction framework performing reconstruction and camera tracking for monocular RGB videos at almost 10 FPS. MAGiSt3R relies on a feedforward model from the 3R family to process RGB videos and regress local point maps, and on a merging model, MAGMA, that combines local maps at both intra-agent and inter-agent levels to obtain the final, global point map. Furthermore, MAGiSt3R performs pose graph optimization to mitigate cumulative camera drift occurring along the feedforward pipeline. We evaluate MAGiSt3R on both synthetic and realworld datasets, demonstrating its superior reconstruction and camera tracking accuracy compared to state-of-the-art feed-forward approaches.

ECCV 2026

DSeq-JEPA: Discriminative Sequential Joint-Embedding Predictive Architecture

Xiangteng He, Shunsuke Sakai, Shivam Chandhok, Sara Beery, Kun Yuan, Nicolas Padoy, Tatsuhito Hasegawa, Leonid Sigal

Recent advances in self-supervised visual representation learning have demonstrated the effectiveness of predictive latent-space objectives for learning transferable features. In particular, Image-based JointEmbedding Predictive Architecture (I-JEPA) learns representations by predicting latent embeddings of masked target regions from visible context. However, it predicts target regions in parallel and all at once, lacking ability to order predictions meaningfully. Inspired by human visual perception, which attends selectively and progressively from primary to secondary cues, we propose DSeq-JEPA, a Discriminative Sequential Joint-Embedding Predictive Architecture that bridges latent predictive and autoregressive self-supervised learning. Specifically, DSeq-JEPA integrates a discriminatively ordered sequential process with JEPA-style learning objective. This is achieved by (i) identifying primary discriminative regions using an attention-derived saliency map that serves as a proxy for visual importance, and (ii) predicting subsequent regions in discriminative order, inducing a curriculum-like semantic progression from primary to secondary cues in pre-training. Extensive experiments across tasks – image classification (ImageNet), fine-grained visual categorization (iNaturalist21, CUB, Stanford Cars), detection/segmentation (MS-COCO, ADE20K), and low-level reasoning (CLEVR) – show that DSeq-JEPA consistently learns more discriminative and generalizable representations compared to I-JEPA variants.

ECCV 2026

Mitigating Pose–Scale Discrepancy Bias and Reforming Multi-Support Reasoning for Few-Shot Semantic Segmentation

Shreya Biswas, Zhaozheng Yin

Few-shot semantic segmentation (FSS) often degrades when support and query images exhibit large pose or scale differences since conventional prototype matching operates in geometry-entangled feature spaces. To mitigate this brittleness, we propose a lightweight encoderdecoder framework that disentangles object representations into a geometryinvariant semantic code and a low-dimensional modulation code capturing instance-specific geometric variation. The reconstruction decoder is trained with equivariance constraints so that geometric information gets encoded by the modulation pathway while preserving semantic consistency in a separate branch. As a result, robustness to pose-scale discrepancies is improved before segmentation without explicit cross-image alignment. To further strengthen supervision from limited supports, we introduce a refinement module that synthesizes lightweight augmented views of each support and performs cyclic ensemble refinement to generate more stable predictions. Predictions from multiple supports are then fused using spatially adaptive reliability weighting, producing cleaner and better-aligned query predictions. Across standard FSS benchmarks, our method consistently improves performance - particularly under large viewpoint changes. Ablations confirm that the disentanglement combined with cyclic ensemble and spatial refinement are critical to the gains. Project Website.

ECCV 2026

Beyond Aesthetics: Quantifying Information Loss in Turbid Scenes

Vasiliki Ismiroglou, Tasos Benos, Malte Pedersen, Stefan Bengtson, Thomas B. Moeslund

Visibility in underwater environments degrades rapidly under turbid conditions, yet the effects on computer-vision models remain unclear. This issue is compounded by reliance on synthetic turbidity datasets, which may misrepresent real-world information loss. To address this gap, we introduce the Turbid Underwater Baseline (TUB) dataset, comprising 1,320 images captured under extreme turbidity and over 16,000 high-confidence ground-truth segmentation masks. We additionally propose PCD, a metric derived from phase congruency maps that is invariant to contrast and aims to capture the loss of structural information in real turbidity. We show that PCD correlates strongly with the performance of instance segmentation models on both real and synthetic turbid images, whereas common metrics in the field show weak to no correlation at all. The dataset and relevant code can be found on the project page: https://vap.aau.dk/pcd

ECCV 2026

Physics-Guided Deep Learning for Linear Mueller Matrix Acquisition

Yuan Liang, Shaoli Liu, Jiachun Huang

The Mueller matrix encodes rich structural and physical information, but its rapid and accurate estimation remains challenging. While physics-based polarimetric Bidirectional Reflectance Distribution Function (pBRDF) models provide useful formulation constraints, their application is limited by inaccessible parameters and model mismatch. To address this challenge, we propose a physics-guided two-stage framework for single-shot linear Mueller matrix recovery. Given one set of four polarization images and an object mask under a fixed known incident polarization state, the first stage estimates pBRDF-related parameters and uses an analytical pBRDF model to construct a structured Mueller initialization. A second network then refines this initialization through residual correction. Evaluated on a newly constructed hybrid real-synthetic dataset, our method improves matrix reconstruction, observed-state rendering, held-out forward Stokes prediction, and physical-plausibility diagnostics over adapted pBRDF fitting baselines. Downstream validation through shape from polarization (SfP) and material classification further provides secondary evidence that the recovered matrices preserve useful polarimetric cues for geometric and material analysis.

ECCV 2026

Physically Grounded Monocular Depth via Nanophotonic Wavefront Encoding

Bingxuan Li, Jiahao Wu, Yuan Xu, Zezheng Zhu, Yunxiang Zhang, Kenneth Chen, Yanqi Liang, Nanfang Yu, Qi Sun

Depth foundation models (DFMs) offer strong learned priors for 3D perception from single RGB images but lack physical depth cues, leading to ambiguities in metric scale. We introduce metalenses, an emerging class of ultrathin planar optical elements, as a solution to physically encode missing metric depth cues via nanophotonics. In this paper, we bridge the gap between metalens and DFMs to achieve accurate metric monocular depth sensing. In a single monocular shot, our metalens embeds depth-dependent positional shifts into two polarized optical wavefronts. With an input adaptation strategty, we enable direct fine-tuning that aligns a pretrained DFM with the optical signals. To scale the training data, we further develop a comprehensive simulation pipeline that synthesizes metalens responses from RGB-D datasets, incorporating physical factors to minimize the sim-to-real gap. Experiments demonstrate that this approach outperforms both monocular metric depth estimation and depth-from-defocus baselines, showing an effective pathway for accurate monocular metric depth sensing.

ECCV 2026

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

Wuyi Liu, XU HAN, Yuren Chen, Yige Mao, Zishuo Peng, Xianzhi Li

Image morphing aims to produce a smooth and semantically consistent transition between two input images. Existing diffusionbased 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.

ECCV 2026

Learning to Recover Task Experts from a Multi-Task Merged Model

Jinwook Jung, Taegyu Kim, Kumju Jo, Sungyong Baik

Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suf-fers from parameter interference. While dynamic merging models aimto bridge this gap, many works rely on the costly storage and loadingof redundant expert components at inference. In this work, from theperspective of task expert, we view parameter interference as parame-ter perturbation introduced to each expert during merging process. Weshow that such parameter perturbations can be modeled as affine trans-formation, which can be approximated as additive offsets. Motivatedby these, we propose Recover Task eXpert (ReTeX), a frameworkthat predicts those offsets, in order to undo parameter interference andrecover task-expert performance from a single merged checkpoint. Torecover the appropriate expert when task identity is unknown, we in-troduce a router-free task identifier based on SVD subspace signaturescomputed offline before inference. At inference, the identifier selects thetask whose subspace yields the smallest projection residual for a giveninput. As a result, ReTeX recovers over 95% of individual-expert per-formance in both vision and NLP domains, while significantly improvinggeneralization to unseen tasks. Crucially, we also show that the param-eter offset prediction leads to emergent adaptive interpolation of expertknowledge for out-of-distribution (OOD) tasks. ReTeX adaptively in-terpolates seen expert knowledge to handle unseen tasks. Our code isavailable at https://github.com/BAIKLAB/ReTeX

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