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ECCV 2026

NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation

shuheng zhang, Feng Wu

Interactive 3D segmentation aims to extract object masks inpoint clouds with minimal user clicks. Despite recent progress, most ex-isting approaches still struggle with (i) coarse voxel resolution that blursfine boundaries under limited clicks and (ii) hard false positives causedby confusing background structures. These issues are exacerbated bydensity and scale shifts across datasets (e.g., dense RGB-D reconstruc-tions vs. sparse LiDAR scans), where fixed refinement heuristics andpurely click-driven decoding generalize poorly. To address them, we pro-pose NegROI — a novel transformer-based interactive framework thatcouples click-centric multi-resolution refinement with scene-conditionednegative prompts. Given a coarse voxel prediction, it refines only a lo-cal Region Of Interest (ROI) around the current click on a finer gridand fuses refined logits back to the coarse mask. To improve robustnessand efficiency, we introduce uncertainty-driven selective refinement thatprioritizes ambiguous regions. Meanwhile, we model hard backgroundpatterns via a set of scene-conditioned negative prompts obtained bycross-attention over scene tokens. We further stabilize these prompts witha diversity regularizer. Finally, we propose boundary-aware hard nega-tive mining to supervise negative-prompt attention toward boundary-proximal, high-confidence false positives. Our experiments on commonbenchmark datasets (i.e., ScanNet, S3DIS, and KITTI) demonstrate im-proved click efficiency and reduced false positives, with stronger cross-dataset robustness than the state-of-the-art baselines.

ECCV 2026

BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis

Kunyu Zhang, Tianxiang Xu

Multi-site functional MRI (fMRI) studies are essential forrobust neuropsychiatric diagnosis yet suffer severe domain shifts fromscanner heterogeneity, demographics, and site-specific acquisition pro-tocols. Traditional domain adaptation requires concurrent source andtarget data access, violating clinical privacy regulations. Moreover, func-tional connectivity matrices lie on the Symmetric Positive Definite (SPD)manifold, where Euclidean operations cause geometric distortions cor-rupting diagnostic patterns. We propose BrainRiem, a source-free do-main adaptation framework learning compact Riemannian brain pro-totypes via manifold-aware bi-level optimization. It employs the Log-Euclidean Metric to ensure prototypes remain valid SPD matrices, whileDirichlet Energy spectral calibration aligns their frequency characteris-tics with real brain networks. Only anonymized prototypes are trans-mitted to target sites, serving as stable anchors for training local mod-els without source data access and reducing leakage under the evalu-ated attacks. Comprehensive experiments on ABIDE and REST-meta-MDD show BrainRiem consistently outperforms state-of-the-art source-free, traditional, and graph domain adaptation methods across diversescanners and demographics. Notably, learned prototypes exhibit biolog-ically interpretable connectivity patterns aligning with established neu-roscience findings, validating the necessity of Riemannian geometry forbrain network analysis.

ECCV 2026

When Specialists Meet Generalists: Segmenter-Coordinated Asymmetric Learning for Label-Deficient Concealed Object Segmentation

Chunming He, Dingming Zhang, Longxiang Tang, Ziyun Yang, Fengyang Xiao, Sina Farsiu

Existing label-deficient concealed object segmentation (LDCOS) methods either rely on consistency constraints within a meanteacher framework or employ the Segment Anything Model (SAM) as a fixed pseudo-label generator. Interestingly, task-specific segmenters and foundation models exhibit complementary failure modes: segmenters lack generalization under scarce labels, while SAM suffers from domain gaps in concealed scenes. A natural solution is to employ co-training to mutually promote both models. However, we observe that standard homogeneous co-training cannot exploit this complementarity because architecturally identical networks tend to share similar error patterns, especially when targets are heavily concealed. To address this, we present SCALER (Segmenter-Coordinated Asymmetric LEaRning), a framework that jointly optimizes a mean-teacher segmenter and a learnable SAM through two alternating phases with model-specific optimization strategies. In Phase I, the segmenter is updated under fixed SAM supervision using entropy-based image-level and uncertainty-based pixellevel weighting to suppress unreliable pseudo-label regions. In Phase II, SAM is updated via an augmentation invariance loss and a noise resistance loss, which exploit SAM’s inherent perturbation robustness rather than treating it identically to the segmenter. This asymmetric design is the key distinction from prior co-training and one-way distillation methods: each model’s learning strategy is tailored to its own inductive bias, enabling mutual enhancement. Experiments across eight LDCOS tasks demonstrate consistent gains, and SCALER improves both the lightweight segmenter and the foundation model, serving as a general plug-and-play paradigm for label-scarce conditions. The code is available at https://github.com/ChunmingHe/SCALER.

ECCV 2026

What Moves? Localized Motion Representations for Compositional Scene Control

Frank Fundel, Malek Ben Alaya, Thomas Ressler-Antal, Stefan Andreas Baumann, Bjorn Ommer

Real-world dynamics are inherently compositional: multiple entities move simultaneously within a shared scene, each exhibiting distinct motion patterns. Yet most existing video representations encode motion globally, without explicitly capturing localized motion for individual entities. Crucially, motion is defined relative to a global reference frame, including camera motion and scene layout. However, localized embeddings are often computed from cropped images or obtained by masking features after encoding, discarding the context needed to interpret motion. To address this, we introduce a promptable localized motion representation that produces persistent embeddings for user-specified regions defined by spatial masks. Rather than cropping the input or masking features, our model processes the full video and conditions motion encoding directly on the queried region. This yields temporally consistent, region-addressable embeddings that isolate local dynamics while retaining the global context required for disambiguation. We demonstrate object-level motion transfer, enabling controlled composition of dynamic scenes. Beyond generative control, our embeddings support localized action classification in multi-actor videos. Across both tasks, our approach improves controllability and outperforms global representations localized through cropping or post-hoc masking. Project page: https://compvis.github.io/WhatMoves

ECCV 2026

VNC: A Scale-Space Foundation for Learnable 3D Surface Evolution

Baoxing Li, Yong Deng, Xu Zhao

Controlling continuous, physically valid 3D surface evolutionis a fundamental challenge in computer vision and graphics. Current neu-ral representations typically rely on unstructured latent spaces or discreteiterations, lacking explicit geometric interpretability and struggling toproduce smooth deformations. While classical scale-space theory offersclear physical interpretability, its numerical irregularity isolates it frommodern deep learning. In this paper, we bridge this gap by establishing alearnable scale-space foundation. Our framework comprises three syner-gistic components: (1) the Variational Neighborhood Curvature (VNC)operator, an efficient, parallelizable, and scale-stable geometric metric,(2) the spacetime-balanced VNC-Flow algorithm, which translates ir-regular physical smoothing into structured deformation trajectories, and(3) a Controllable Variational Autoencoder (C-VAE) that learns thesetrajectories conditioned on normalized evolution time. Departing fromtraditional discrete iterations or unstructured latent interpolations, ourcontinuous neural surrogate induces a structured radial latent organiza-tion. This enables precise control over continuous shape abstraction andestablishes a structural prior for downstream applications.

ECCV 2026

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You

Chain-of-Thought (CoT) prompting has proven remarkablyeffective for eliciting complex reasoning in large language models (LLMs).Yet, its potential in multimodal large language models (MLLMs) re-mains largely untapped, hindered by the absence of large-scale datasetsthat capture the rich, spatially grounded reasoning intrinsic to visual un-derstanding. Existing visual-CoT resources are typically small, domain-specific, or lack the structured stepwise supervision necessary for com-positional visual reasoning. In this paper, we introduce VisReason, alarge-scale dataset designed to advance visual Chain-of-Thought rea-soning. VisReason comprises 489K annotated examples spanning fourdiverse domains, each featuring multi-round, RoI-grounded rationalesthat guide MLLMs through interpretable visual reasoning steps. Build-ing upon this, we curate VisReason-Pro, a 165K subset produced witha stronger GPT annotator, enriched with detailed reasoning traces anddepth-augmented spatial annotations derived from monocular depth andsegmentation cues. Fine-tuning strong MLLM backbones on VisRea-son and VisReason-Pro yields substantial improvements in step-by-stepvisual reasoning accuracy, RoI localization, interpretability, and fine-grained/spatial reasoning performance. These results demonstrate thatVisReason equips MLLMs with more systematic and verifiable visualreasoning capabilities. We envision VisReason as a cornerstone for cul-tivating human-like visual reasoning, paving the way toward the nextgeneration of multimodal intelligence.

ECCV 2026

VGGRPO: Towards World-Consistent Video Generation with 4D Latent Reward

Zhaochong An, Orest Kupyn, Théo Uscidda, Andrea Colaco, Karan Ahuja, Serge Belongie, Mar Gonzalez Franco, Marta Gazulla

Large-scale video diffusion models achieve impressive visualquality, yet often fail to preserve geometric consistency. Prior approachesimprove consistency either by augmenting the generator with additionalmodules or applying geometry-aware alignment. However, architecturalmodifications can compromise the generalization of internet-scale pre-trained models, while existing alignment methods are limited to staticscenes and rely on RGB-space rewards that require repeated VAE de-coding, incurring substantial compute overhead and failing to general-ize to highly dynamic real-world scenes. To preserve the pretrained ca-pacity while improving geometric consistency, we propose VGGRPO(Visual Geometry GRPO), a latent geometry-guided framework forgeometry-aware video post-training. VGGRPO introduces a Latent Ge-ometry Model (LGM) that stitches video diffusion latents to geometryfoundation models, enabling direct decoding of scene geometry from thelatent space. By constructing LGM from a geometry model with 4D re-construction capability, VGGRPO naturally extends to dynamic scenes,overcoming the static-scene limitations of prior methods. Building onthis, we perform latent-space Group Relative Policy Optimization withtwo complementary rewards: a camera motion smoothness reward thatpenalizes jittery trajectories, and a geometry reprojection consistencyreward that enforces cross-view geometric coherence. Experiments onboth static and dynamic benchmarks show that VGGRPO improvescamera stability, geometry consistency, and overall quality while elim-inating costly VAE decoding, making latent-space geometry-guided re-inforcement an efficient and flexible approach to world-consistent videogeneration. Our project page is here.

ECCV 2026

Unlocking Complex Image Editing via Natively Interleaved Visual Textual CoT with Deep Confidence Reasoning

Zhentao Zou, Zhengrong Yue, Kunpeng Du, Binglei Bao, Hanting Li, Haizhen Xie, Guozheng Xu, Yue Zhou, Jie Hu, Xue Jiang, Xinghao Chen

Language guided image editing has gained significant pop-ularity, yet existing methods struggle with intricate object intersectionsand fine grained spatial relationships due to the absence of an explicitvisual reasoning process. While Chain of Thought (CoT) has been ex-plored to enhance reasoning, purely textual CoT or coordinate basedprompts are fundamentally limited in representing intricate visual lay-outs and lack the pixel level cues necessary for precise editing. To addressthese challenges, we propose Unlocking Complex Image Editing viaMultimodal Reasoning Edit (MURE), a natively multimodal frame-work that shifts the editing process from purely verbal reasoning to asequence of native interleaved textual and visual rationales. As aunified monolithic model, MURE enables seamless end to end optimiza-tion by sharing a latent space and reusing KV cache, which provides amore flexible intermediate visual process and mitigates the error prop-agation typical of multi tool systems. To further ensure reasoning reli-ability, we introduce the Multimodal Deep Confidence (MMDC)paradigm. This paradigm explores a tree of visual reasoning paths ateach step, pruning low quality branches via a deep confidence score froma reward model to ensure a high fidelity trajectory toward the final re-sult. We define the formal representation for native interleaved chainsand release CoT-Edit 14K, the first dataset comprising 14K high qual-ity multimodal reasoning examples. Extensive experiments demonstratethat MURE establishes a new state of the art in complex editing, achiev-ing an average gain of 7.5% on the MagicBrush benchmark and show-casing superior spatial reasoning capabilitiesThe code and CoT-Edit-14Kdataset are released at https://github.com/zhentao-zou/MURE.

ECCV 2026

Towards Real-World Wearable Motion Reconstruction

Andrea Boscolo Camiletto, Rishabh Dabral, Eduardo Alvarado, Thabo Beeler, Marc Habermann, Christian Theobalt

The modern-day surge in popularity of wearable devices posesa fundamentally unique motion capture problem: reconstructing full-body movement from any set of sensing hardware worn at a given mo-ment. Yet, most research efforts assume fixed sensor configurations (e.g.,IMU suits or HMD-centric rigs) and cannot generalize across them.In contrast, we argue that motion capture should prioritize unobtru-sive and lightweight devices such as smartphones, smartwatches, smartglasses, and smart insoles, and study the interplay between them. To thisend, we make three contributions. First, we present a large-scale multi-modal dataset synchronizing these consumer-grade sensors with ground-truth 3D motion, spanning 50 diverse activities including everyday tasks,sports, and social interactions. Second, we propose WHIP, a baseline gen-erative model that reconstructs motion from arbitrary subsets of avail-able sensors, robustly handling missing modalities and producing physi-cally plausible motions. Third, we conduct a systematic study of sensorcomplementarity, quantifying how different modalities complement oneanother. Code and dataset are available at this URL.

ECCV 2026

Label-Free Text Prototype Adaptation for Open Vocabulary Segmentation

Keli Wang, Meixuan Li, Tianyu Li, Guoqing Wang

Vision-language models (VLMs) have recently been adoptedfor open-vocabulary segmentation by aligning visual features with textprototypes. However, this alignment often degrades under domain shift,as text prototypes encode language priors learned from natural imagesthat may not hold in the target domain. To address this challenge, wepropose VPTA, a framework that adapts text prototypes to the tar-get domain to mitigate language prior bias using only unlabeled target-domain images. VPTA gathers reliable visual evidence from confidentpixels, estimates per-class evidence reliability to drive class-dependentprototype updates, and explicitly preserves prototype separation to pre-vent collapse between similar categories. This lightweight adaptationproceeds through a small number of iterations with negligible computa-tional overhead. We conduct extensive evaluations under diverse domainshifts across three representative domains, including remote sensing, au-tonomous driving, and natural scene segmentation. VPTA achieves state-of-the-art performance on ten remote sensing datasets and consistentlyimproves segmentation performance on autonomous driving and naturalscene benchmarks. These results demonstrate the strong generalizationability of VPTA and highlight its effectiveness as a practical solution forlabel-free prototype adaptation in vision-language models.

ECCV 2026

Virtual Category-Guided Continual Generalized Category Discovery

Jiahui Xiong, Qiuxia LAI, Hongsong Wang

Continual Generalized Category Discovery (C-GCD) aimsto incrementally identify novel categories from sequential unlabeled datawhile preserving recognition of known classes, which is an essential ca-pability for open-world visual learning. A major bottleneck lies in am-biguous unlabeled samples that cannot be confidently assigned to knownclasses nor reliably grouped as novel ones, making pseudo-labeling brit-tle and often biasing learning toward familiar categories. In this work,we introduce Virtual Category-Guided Continual Generalized CategoryDiscovery by adapting Virtual Category Learning (VCL) to the contin-ual setting. Our method identifies uncertain samples and assigns them totemporary virtual categories, enabling safe and informative learning fromunlabeled streams without injecting noisy labels, while improving unla-beled data utilization and mitigating prediction bias. To further stabilizediscovery across sessions and enhance class separation, we augment VCLwith Expanded Neighborhood Contrastive Learning (ENCL), which ex-ploits extended neighborhood relations and an adaptive margin to learnmore discriminative and well-separated representations for both old andemerging classes. Extensive experiments on CIFAR-100, Tiny ImageNet,and ImageNet-100 demonstrate that our approach consistently outper-forms state-of-the-art methods, establishing a scalable and effective so-lution for C-GCD. Code is on: https://github.com/Mrxjh105/VC-CGCD

ECCV 2026

Revisiting Deepfake Detection: BCNet for Robust Generalization Beyond Semantic Dependence

Jian Yang, Shibo Yao, Renshuai Tao, Chuangchuang Tan, Yao Zhao

Recent deepfake detection methods leveraging vision foun-dation models (VFMs) like CLIP have made significant progress. How-ever, the abundant semantic information in large training datasets hasmade VFMs highly dependent on semantics. As a result, VFM-basedmethods perform well on images from the same category as the train-ing set but struggle with others. This bias limits generalization in real-world scenarios. To address this issue, we propose the Basis CorrectionNetwork (BCNet), which consists of two modules: the attention-guidedsemantic erasure (ASE), which adaptively identifies and erases seman-tic regions of the image by capturing the model’s semantic attention,and the normalized-gradient perturbation enhancement (NPE) scalesthe gradients of fake samples to concentrated values and adds them asa small perturbation to the original sample, helping the model recog-nize more forgery patterns and improving its ability to distinguish fakefrom real samples. This design ensures the model focuses on the coredistinction between real and fake categories rather than semantic infor-mation. Extensive experiments on 51 AI-generated datasets show thatBCNet achieves 96.7% generalization accuracy on WildRF (collectedfrom real social media) and outperforms other competitors by 8.7% onAIGI-Bench, offering a fresh perspective on semantic generalization indeepfake detection. The code is open-sourced and publicly available athttps://github.com/rstao-bjtu/BCNet.

ECCV 2026

TopoGAT: Plug-and-Play Topological Graph Attention for Fine-Grained 3D Segmentation

Xinyu Jiang, Lech Szymanski, Steven Mills

Fine-grained 3D point cloud segmentation is essential forrobot manipulation and CAD editing. A key unresolved challenge is thatdifferent parts of the same object often share similar local geometry, mak-ing part boundaries difficult to distinguish. Existing methods constructlocal patches via K-nearest search and rely on deeper and larger networksto learn discriminative features. However, these approaches mainly focuson local geometry and lack global structural awareness, which leads toambiguous predictions under sampling noise and partial observations.In this work, we propose Topological Graph Attention Network (To-poGAT), a lightweight, plug-and-play backbone refinement module thatintegrates topological data analysis with Graph Attention Networks tointroduce global structural information into point-wise feature learning.When combined with existing backbones, TopoGAT improves segmenta-tion accuracy with a slight parameter increase. Extensive experiments onfine-grained 3D part segmentation validate the effectiveness of the pro-posed TopoGAT and show up to 1.0% improvement on ShapeNetPartdataset and 3.3% improvement on S3DIS dataset.

ECCV 2026

Structured-Noise Masked Modeling for Video, Audio and Beyond

Aritra Bhowmik, Carlos Hinojosa, Fida Mohammad Thoker, Bernard Ghanem, Cees Snoek

Masked modeling has emerged as a robust self-supervisedlearning framework. However, most methods rely on random masking,which disregards the structural properties of different data modalities. Toalign with the spatiotemporal and spectral characteristics of video andaudio data, we introduce a structured noise-based masking approach.By filtering white noise into different color noise distributions, we gen-erate structured masks that capture modality-specific patterns withoutrequiring handcrafted heuristics or access to the data. Our approach en-hances masked video and audio modeling frameworks without any addi-tional computational cost. Experiments show that structured noise mask-ing consistently outperforms random masking, underscoring the value ofmodality-aware masking strategies for representation learning.

ECCV 2026

SAM2Matting: Generalized Image and Video Matting

Ruiqi Shen, Guangquan Jie, Chang Liu, Henghui Ding

Despite impressive advances in image matting, video mattingremains challenging due to the inherent gap between high-level tracking,which requires frame-wise understanding, and low-level matting, whichfocuses on extremely fine-grained details. Existing methods attempt thisusing costly domain-specific video matting datasets, which may limittheir out-of-domain generalization and leave tracking robustness vulner-able. We rethink this paradigm with SAM2Matting, a novel frameworkthat decouples the task by enhancing the foundational tracker of SAM2with a region-proposal bridge and dedicated matting heads. This en-ables uncompromised SAM2 to handle tracking while the matting com-ponents focus exclusively on resolving fine-grained intricate details. No-tably, despite being trained only on images, SAM2Matting establishesnew state-of-the-art performance on video matting, with robust general-ization across both human-centric and in-the-wild matting scenarios.

ECCV 2026

Iterative Refinement of Semantic and Spatial Representations for Open-Vocabulary Camouflaged Object Segmentation

Fangyan Wang, Ge Jiao, Guowen Yue

Open-Vocabulary Camouflaged Object Segmentation aimsto segment camouflaged objects from unseen categories. Existing two-stage methods typically treat category semantics as a one-time priorfor segmentation, thereby lacking deep and iterative interaction betweentextual semantics and visual spatial structures. To address this limita-tion, we propose an iterative two-stage refinement framework based onsemantic context and spatial structure. Specifically, we first design aspatial-structure-aware category re-ranker that leverages segmentationmasks generated by SAM2 to reorder CLIP’s candidate categories ac-cording to region-level visual consistency. Furthermore, we introduce asemantic-context-based segmentation modulator that injects the refinedcategory information into SAM2, guiding SAM2 to produce more preciseand discriminative segmentation results. Notably, the proposed re-rankerand modulator are jointly optimized in an iterative manner, forming aclosed-loop refinement process that enables mutual guidance between se-mantic representations and spatial features. Through iterative updates,semantic context and spatial structure are progressively enhanced. Ex-tensive experiments demonstrate that the proposed method significantlyoutperforms existing open-vocabulary methods and camouflaged objectsegmentation approaches. In particular, the proposed method achieves a14.4% improvement in the cSm metric compared with OVCoser.

ECCV 2026

SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selection

Kim Jun-Seong, Tae-Hyun Oh, Eduardo Pérez-Pellitero, Youngkyoon Jang

We propose Self-Augmented Residual 3D Gaussian Splat-ting, a novel framework for stabilizing uncertainty quantification andenhancing uncertainty-aware supervision in Next-Best-View selection foractive scene reconstruction. To efficiently estimate scene coverage, SA-ResGS generates geometry-consistent Self-Augmented point clouds (SA-Points) via triangulation between observed training views and rasterizedextrapolated views. To address the lack of learning signals in underrep-resented regions within sparse, wide-baseline settings, we introduce thefirst skip-connection-inspired residual learning strategy tailored for 3DGS.This mechanism amplifies gradient flow to weakly contributing, high-uncertainty Gaussians. Our contributions are threefold: (1) a physicallygrounded, diversified view selection strategy; (2) an uncertainty-awareresidual supervision scheme that improves gradient flow and learningstability; and (3) implicitly debiased uncertainty quantification resultingfrom constrained view selection and residual supervision. Experiments onNeRF Synthetic, Mip-NeRF 360, and challenging extended benchmarkfrom Deep Blending and Tanks and Temples demonstrate that SA-ResGSconsistently outperforms state-of-the-art competing methods in bothreconstruction quality and view selection robustness.

ECCV 2026

Rethinking Cross-Spectral Image Generation via Shared-Specific Representation

Haining Wang, Na Li, Huijie Zhao, Yifan Da, Yan Wen, Yi Su, Yuqiang Fang

Cross-spectral image synthesis from visible to infrared facessigni_x001C_cant challenges due to the severe scarcity of infrared data in criti-cal scenarios. Existing methods primarily achieve style transfer throughpixel-level mappings, which often su_x001B_er from semantic misalignment andunrealistic spectral characteristics. To address this problem, we rethinkcross-spectral generation as a decoupling and reconstruction process ofshared-speci_x001C_c features between modalities and propose SHASP, a novelgenerative network. Speci_x001C_cally, an encoding-decoupling module is de-signed at the front of the generator, which consists of a structure contentencoder and a spectral feature encoder to decouple the shared-speci_x001C_cfeatures. A decoding-reconstruction module is designed at the backendof the generator to perform feature combination and reconstruction. Wefurther impose semantic consistency constraints on shared features toensure precise cross-modal alignment. Extensive experiments on aerial,driving, and monitoring scenarios demonstrate that our method outper-forms state-of-the-art approaches. This work reveals that there are com-mon representations that can be mined at the semantic structure levelin cross-spectral data, and modality gaps can be accurately modeled viaa decoupling-reconstruction mechanism.

ECCV 2026

ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration

Qicheng Zhao, Yu Li, Qi Sun, Zheyu Yan

The adoption of powerful diffusion models is hindered bytheir significant inference latency. Recent “cache-then-forecast” schemesalleviate this issue by accelerating DiTs using derivative-based polynomi-als, but they suffer from severe quality degradation at high accelerationratios. Our analysis reveals its root cause: the discrete extrapolation per-formed on representations that are misaligned with the continuous diffu-sion trajectory and are numerically unstable. Thus, accelerated DiTs suf-fer from accumulated spatial errors, noisy derivative amplification, andhigh-order instability. We therefore reformulate accelerated inference asstable macro-trajectory extrapolation in ordinary differential equation(ODE) space. Instead of predicting intermediate features, we align fore-casting with the model’s Global Drift (GD), i.e., the end-to-end stateevolution, thereby eliminating feature inconsistency and memory over-head. However, even this smooth macro-trajectory remains vulnerableto the derivative fallacy: its higher-order temporal derivatives are intrin-sically noisy. Thus, we introduce a derivative-free barycentric Lagrangeextrapolator to effectively bypass derivative instability and approxima-tion error. We further propose a bounded Phase Mapping that regu-larizes the extrapolation domain, suppressing oscillatory error growth.These elements collectively constitute ResilPhase, a noise-resilient ac-celeration framework. Experiments on FLUX.1-dev and HunyuanVideodemonstrate state-of-the-art fidelity under aggressive acceleration ratios.Code is publicly available at https://github.com/zqc214/ResilPhase.

ECCV 2026

ReconSplat: Generalizable 3D Scene Reconstruction Beyond Observed Views

Giuseppe Stracquadanio, Kevin Raj, Julia Grabinski, Stefan Roth

We introduce ReconSplat, a feed-forward model for 3D scenereconstruction that aims to address the longstanding trade-off betweenplausible view generation for unobserved regions and geometric consis-tency, providing both geometrically aligned novel views and sharp depthestimates. Our approach builds on 3D Gaussian splatting (3DGS) as anintermediate differentiable scene representation and integrates it with amulti-view latent diffusion model (MV-LDM) trained to act simultane-ously as a refiner and an inpainter for appearance and scene geometry.We enforce geometric consistency by guiding the diffusion process withvariational 3D latent features for appearance and geometry, encoded bythe feed-forward 3DGS representation and rasterized to 2D latent space.ReconSplat produces both photorealistic novel views and accurate depthmaps on real-world benchmarks, RealEstate10K and DL3DV-10K, out-performing existing methods in challenging extrapolation setups. No-tably, ReconSplat allows the extrapolation of unseen and challengingviewpoints jointly with coherent and precise scene geometry. Code andadditional visual results are available on our project page.

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