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ECCV 2026

Wavelet-Guided Semantic Signal Compensation for Inversion-Free Image Editing

Anqi Tang, Wenhao Sun, Zhaoqiang Liu

Text-guided image editing aims to modify visual content ac-cording to a target prompt while preserving the background. Recentinversion-free image editing frameworks such as FlowEdit have demon-strated strong editing capability without requiring inversion. Empirically,FlowEdit can achieve substantial semantic changes under appropriatehyperparameter settings. However, we observe that under certain globalattribute shifts, the editing trajectory may not effectively move awayfrom the source distribution in the early timesteps. Our analysis sug-gests that in the high-noise regime, the dominant manifold-seeking flowtoward the data manifold can reduce the influence of the text-conditioneddirection, leading to limited global modification while background struc-tures remain only moderately preserved. Inspired by this observation,we propose an inversion-free, frequency-aware semantic compensationstrategy that strengthens the effective signal in the early stage of gener-ation, while maintaining structural consistency in the background. Theproposed method improves global editing capacity without sacrificingbackground fidelity.

ECCV 2026

Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metrics

Tim Alexander Bader, Tim Eberhardt, Maximilian Dillitzer, Wilhelm Stork

Camera-based perception systems for autonomous drivingare typically developed and evaluated using fixed sensor rigs, while real-world vehicle fleets exhibit substantial variation in camera placement,orientation, field of view, and camera count. This mismatch introducesa cross-rig domain gap in which only the geometric observation pro-cess changes. To study this effect under controlled conditions, we intro-duce Plentiful CARLA Camera Rigs, a benchmark that renders identicaldriving scenes under 14 systematically designed camera rigs. This setupenables direct analysis of cross-rig generalization without confoundingchanges in scene content or appearance. Using the benchmark, we ana-lyze cross-rig transfer behavior of representative multi-view perceptionarchitectures and observe substantial performance shifts induced by ge-ometric rig variation. To facilitate structured analysis, we further intro-duce two calibration-based descriptors derived from rig metadata: RigVariance, capturing internal rig diversity, and Rig Contrastive Distance,measuring geometric discrepancy between rigs. Our experiments showthat geometric rig differences strongly correlate with relative cross-rigperformance shifts and that Rig Contrastive Distance provides a reliableproxy for ranking transfer difficulty between sensor rigs.

ECCV 2026

Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection

Manwen Yang, Leqian Ding, Yu Guo, Fei Wang

Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to object-centric bias,normal and anomalous text prototypes exhibit a high semantic over-lap. While enforcing strict orthogonality between them improves dis-criminability, mapping highly contiguous visual inputs onto drasticallyorthogonal prototypes introduces a geometric dilemma, disrupting thepre-trained structural continuity. To address this problem, we proposeProximity-CLIP, a framework that visually calibrates the semantic mar-gin to guide visual adaptation. First, we introduce a visually-calibratedsemantic proximity learning mechanism that uses a bounded dynamicregularization to learn an appropriate semantic margin, ensuring dis-criminative separation while preserving structural alignment. Second, wedesign an Anomaly Query Module (AQM) driven by these text priors.Using the calibrated anomalous prototype as a semantic query, the AQMactively retrieves localized defect cues from contextual visual patches,mitigating the dilution of subtle anomalies during global pooling. Exten-sive experiments demonstrate that Proximity-CLIP outperforms currentstate-of-the-art methods across multiple ZSAD benchmarks with mini-mal architectural modifications.

ECCV 2026

Pixel-wise Planarity for High-Precision Monocular Plane Segmentation

Ahmetcan Yavuz, Alpay Ozkan, Remi Pautrat, Shaohui Liu, Marc Pollefeys

Plane segmentation from a single RGB image remains chal-lenging due to imprecise region grouping and geometrically inconsistentsupervision, often leading to over-segmentation and false planar detections.We propose instead a pixel-wise planarity prediction framework for ro-bust monocular plane segmentation. Building on a pretrained monoculargeometric backbone predicting depth and surface normals, we introducea dedicated planarity head that estimates per-pixel planarity confidence.During inference, predicted depth, normals, and planarity are combinedin a lightweight region-growing procedure that enforces geometric consis-tency when forming plane segments. We further analyze existing planeground-truth annotations and demonstrate substantial geometric incon-sistencies under strict distance thresholds. Across multiple datasets, ourmethod achieves improved geometric precision and segmentation qualitycompared to prior state-of-the-art approaches, while improving computa-tional efficiency.

ECCV 2026

MorphJEPA: Morphology-Aware Latent Prediction for Hyperspectral Images

Amartya Ray, Tanmay Mandaliya, Muhammad Haris Khan, Biplab Banerjee

We study learning from limited supervision (LLS) for hyperspectral imagery (HSI): learning transferable representations from abundant unlabeled HSIs in the target sensing domain and then adapting with only a handful of labels per class. Joint-Embedding Predictive Architectures (JEPAs) are appealing for LLS because they learn via latent prediction rather than pixel reconstruction; however, they remain unexplored for HSI. Generic masking or cropping is ill-suited to HSI: band masking corrupts high-dimensional spectra, spatial masking removes entire pixel-level semantics, and dense spatial–spectral extraction can yield highly correlated, degenerate prediction targets. We propose MorphJEPA, the first JEPA tailored to HSI, which uses morphology as the pretext signal by predicting embeddings of pixel-aligned targets generated by per-band morphological opening/closing. This induces a directional structural bottleneck that preserves core topology while suppressing high-frequency nuisances. MorphJEPA couples this objective with a compact disentangled spatial–spectral encoder fused by crossattention and a context-anchored Sketched Isotropic Gaussian Regularizer (SIGReg) to prevent collapse and maintain a well-conditioned latent manifold. Extensive 1/5/10-shot evaluations show consistent gains in intra-scene settings (Houston 2013, Trento) and under cross-scene temporal/spatial shifts (Houston 2013 → 2018, HyRank) for both zero-shot transfer and low-shot adaptation. Project page: https://github.com/ amartya-ray/MorphJEPA.

ECCV 2026

MemoBench: Benchmarking World Modeling in Dynamically Changing Environments

Haoyu Chen, Kaichen Zhou, Hang Hua, Kaile Zhang, Jingwen Qian, Wufei Ma, Haonan Chen, Chunjiang Liu, Yizhou Zhao, Xiaoyuan Wang, Weiyue Li, Alan Yuille, Paul Pu Liang, Yilun Du

Video generation models aspire to simulate dynamic environ-ments, and several benchmarks now evaluate memory consistency acrossframes. However, most assess consistency only while the target remainsin view, and the few that force objects out of view evaluate static sceneswhere nothing changes during occlusion. To bridge this gap, we introduceMemoBench, a diagnostic benchmark built around the disappear-and-reappear paradigm in dynamically changing environments: a target ob-ject undergoes a physical process, disappears from view, and must be cor-rectly recovered in its updated state upon reappearance. We curate 360ground-truth clips spanning synthetic and real-world scenes, and designan evaluation suite combining automated metrics with VQA-based as-sessment across four diagnostic pillars. Evaluation of ten state-of-the-artmodels reveals key insights and open challenges regarding memory con-sistency under the disappear-and-reappear paradigm. Our dataset, code,and leaderboard are available at https://github.com/MemoBench-Team.

ECCV 2026

InSeg: Interactive Refinement via Intent Propagation for Point Cloud Semantic Segmentation

Yudong Liu, Yunfei Li, Ge Gao, Han Huang, Ming Gu

Despite recent progress in point cloud semantic segmenta-tion, obtaining deployment-grade predictions on individual scenes re-mains challenging. Interactive refinement offers a practical solution, yetexisting methods commonly interpret user clicks as single-point super-vision, leading to fragmented updates and requiring redundant user in-teraction. We identify this limitation as the Local Correction Trap. Ourkey insight is that a correction click more naturally expresses an in-tent to revise a semantically coherent region than to relabel an isolatedpoint. Based on this view, we propose InSeg, an interactive refinementframework that translates sparse clicks into reliable region-guided virtualsupervision. InSeg propagates corrective intent through feature affinity,spatial connectivity, and object-level priors to construct denoised virtuallabels. To support stable multi-round refinement, we further introducea history-aware mechanism that combines uncertainty-guided updateswith memory replay, reducing error accumulation and preventing previ-ously corrected regions from being overwritten. Extensive experimentsacross multiple benchmarks show that InSeg consistently improves seg-mentation quality and interaction efficiency, achieving strong gains withonly a few clicks.

ECCV 2026

Improving Adversarial Robustness via Activation Amplification and Attenuation

Taïga Gonçalves, Yongsong Huang, Tomo Miyazaki, Shinichiro Omachi

The existence of adversarial attacks is often attributed tothe presence of non-robust features in neural networks. While prior de-fenses reduce their impact via pruning, masking, or feature recalibra-tion, we instead propose to jointly learn to amplify and attenuate thesesignals through a simple activation scaling mechanism. To this end,we introduce Activation Amplification and Attenuation (A3), alightweight plug-in module that enhances adversarial robustness withminimal modifications of the activations. A3 dynamically rescales theactivations using a learnable mask and a scaling factor derived from theoriginal activation magnitudes. The influence of adversarial perturba-tions can be amplified or attenuated using the same learnable parametersby simply flipping the sign of the scaling operation. The amplified signalsserve as negative references to construct novel contrastive and rankingloss functions. Experimental analysis shows that learning to degrade thepredictions in amplification mode simultaneously improves adversarialrobustness in attenuation mode. Moreover, A3 relies on only a smallnumber of learnable parameters, with most of its behavior being deter-mined by the scaling mechanism rather than additional network capac-ity. Extensive experiments demonstrate that integrating A3 into differentbackbones, datasets, and training methods consistently improves adver-sarial robustness while introducing negligible computational and mem-ory overhead compared to existing plug-in modules. Code is available at:https://github.com/tgoncalv/A3.

ECCV 2026

E-M3RF: An Equivariant Multimodal 3D Re-assembly Framework

Adeela Islam, Stefano Fiorini, MANUEL LECHA SANCHEZ, Theodore Tsesmelis, Stuart James, Pietro Morerio, Alessio Del Bue

3D reassembly is a fundamental geometric problem, and inrecent years it has increasingly been challenged by deep learning meth-ods rather than classical optimization. While learning approaches haveshown promising results, most still rely primarily on geometric features toassemble a whole from its parts. This reliance limits their effectiveness incases where geometric information is incomplete or degraded, such as forsmall or eroded fragments. Additionally, solutions do not impose physi-cal constraints that explicitly prevent overlapping assemblies. To addressthese limitations, we introduce E-M3RF, an equivariant multimodal 3Dreassembly framework that takes as input the point clouds, containingboth point positions and color of fractured fragments, and predicts thetransformations required to reassemble them, using SE(3) flow match-ing. Each fragment is represented by both geometric and color features:i) 3D point positions are encoded as rotation-consistent geometric fea-tures using a rotation-equivariant encoder, while ii) the colors at each3D point are encoded with a transformer. The two feature sets are thencombined to form a multimodal representation. We experimented on fivedatasets: two synthetic datasets, Breaking Bad and Fantastic Breaks,and three real-world cultural heritage datasets, RePAIR, Presious, andFRACTURA, demonstrating that E-M3RF on the RePAIR dataset (themost challenging one) reduces rotation error by 23.1% and translationerror by 13.2%, while Chamfer Distance decreases by 18.4% comparedto competing methods. Project page: https://em3rf.github.io/.

ECCV 2026

Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints

Chenyangguang Zhang, Botao Ye, Boqi Chen, Alexandros Delitzas, Fangjinhua Wang, Marc Pollefeys, Xi Wang

Controllable video generation for complex hand-object interactions is a critical step toward building visual world models. However, existing methods often struggle to achieve fine-grained, 3D-consistent hand articulation in generated videos. By relying on dense 2D trajectories or implicit pose representations, they collapse crucial geometric structures into spatially ambiguous signals, leading to severe motion inconsistencies and hallucinated artifacts under egocentric occlusions. To address this, we propose leveraging sparse 3D hand joints as explicit control signals with three key advantages: explicit geometry to resolve occlusions, an intuitive interface for interactive editing, and cross-embodiment generalization to robotic hands. Built upon this, our efficient control module extracts occlusion-aware features from the source reference frame by penalizing unreliable visual features from hidden joints, and employs a 3Dbased weighting mechanism to handle dynamically occluded target joints during motion propagation. Meanwhile, it directly injects 3D geometric embeddings into the latent space to enforce structural consistency. To facilitate robust training and evaluation, we develop an automated annotation pipeline, yielding 1M high-quality egocentric video clips paired with precise hand trajectories. Experiments demonstrate that our approach outperforms state-of-the-art baselines, generating high-fidelity egocentric videos with realistic hand-object interactions.

ECCV 2026

ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device

Fabio Tosi, Luca Bartolomei, Matteo Poggi, Stefano Mattoccia

Monocular depth estimation has seen remarkable progressthrough foundation models achieving robust zero-shot generalization, yettheir computational demands place them far beyond the reach of em-bedded and mobile platforms. Lightweight alternatives exist, but havebeen developed almost exclusively within single-domain, self-supervisedparadigms, failing silently under domain shift. We present ZipDepth, acompact monocular depth network that bridges this gap by combining anefficient reparameterizable encoder-decoder with large-scale knowledgedistillation from a foundation model over a large multi-domain train-ing set. Comprising just 6.1M parameters, ZipDepth runs at real-timerates from server GPUs to power-constrained devices, achieving the besttrade-off between zero-shot accuracy and deployment efficiency amonglightweight models across five benchmarks, taking a significant step to-wards the accuracy of foundation models with 50× more parameters.

ECCV 2026

World-in-Loop: Online Correction via Event-Triggered World Models for Robust VLA Policies

Shaoqing Xu, Fang Li, Zhi-Xin Yang, Qimao Chen, Yuechen Luo, Zhixiang Duan, Yifan Yang, Long Chen

Large-scale Vision-Language-Action (VLA) models excel atmapping natural language instructions to robotic actions. However, aprimary bottleneck in generalizing these models is the scarcity of high-quality real-world data. While fine-tuning requires massive successfuldemonstrations, the low success rates of current base policies in dy-namic environments often lead to compounding errors and inefficient,low-quality data collection. To enhance both real-world data acquisitionand long-horizon task success, we propose a proactive, online interven-tion framework. Our core innovation is a lightweight, composite WorldModel (WM) acting as an event-triggered “corrector”. Instead of con-tinuous frame-by-frame rollouts across the entire task horizon, our WMintervenes only at critical decision points (e.g., prior to grasping) to eval-uate the proposed action’s feasibility. If a high failure risk is detected, thesystem proactively rolls back to a heal state. A generative model thensynthesizes a short video of a successful future trajectory, which guidesthe robot’s re-attempt via an action decoder. We validate our frameworkacross multiple robotic grasping tasks on both simulation and real-worldsystems. Extensive results demonstrate that deploying world models asactive, on-demand correctors significantly improves task success ratesand efficiently harvests high-quality execution data by rescuing poten-tial failures before they occur.

ECCV 2026

Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioning

Dongki Jung, Jaehoon Choi, Adil Qureshi, Somi Jeong, Dinesh Manocha, Suyong Yeon

We present Wid3R, a feed-forward neural network for multi-view visual geometry reconstruction that supports wide field-of-viewcamera models. Unlike existing methods that assume rectified or pinholeinputs, Wid3R directly models wide-angle imagery without explicit cali-bration or undistortion. Our approach leverages a ray-based representa-tion with spherical harmonics and introduces a novel camera model tokento enable distortion-aware reconstruction. To the best of our knowledge,Wid3R is the first multi-frame feed-forward 3D reconstruction methodthat supports 360◦ imagery. Moreover, we show that conditioning on di-verse camera types improves generalization to 360◦ scenes and alleviatesdata sparsity issues. Wid3R achieves significant performance gains, im-proving AUC@30◦ by up to +33.67 on Zip-NeRF (fisheye) and +77.33 onStanford2D3D (360). Project Page: https://jdk9405.github.io/Wid3R/

ECCV 2026

VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-On

XIAOYE LIANG, Zhiyuan Qu, Mingye Zou, Jiaxin Liu, Lai Jiang, Mai Xu, Yiheng Zhu

As virtual try-on (VTON) advances, a growing number ofreal-world scenarios have emerged, pushing beyond the ability of the ex-isting specialized VTON models. Meanwhile, universal multi-referenceimage editing models have progressed rapidly and exhibit strong gen-eralization in visual editing, suggesting a promising route toward moreflexible VTON systems. However, despite their strong capabilities, thestrengths and limitations of universal editors for VTON remain insuffi-ciently explored due to the lack of systematic evaluation benchmarks. Toaddress this gap, we introduce VTEdit-Bench, a comprehensive bench-mark designed to evaluate universal multi-reference image editing modelsacross various realistic VTON scenarios. VTEdit-Bench contains 24,220test image pairs spanning five representative VTON tasks with progres-sively increasing complexity, enabling systematic analysis of robustnessand generalization. We further propose VTEdit-QA, a reference-awareVLM-based evaluator that assesses VTON performance from three keyaspects: model consistency, cloth consistency, and overall image quality.Through this framework, we systematically evaluate 8 universal editingmodels and compare them with 7 specialized VTON models. Resultsshow that top universal editors are competitive on conventional tasksand generalize more stably to harder scenarios, but remain challengedby complex reference configurations, especially multi-cloth conditioning.

ECCV 2026

Vitality-Aware Compression for Efficient Image-to-Shape Diffusion Transformers

Jaeah Lee, Hyunjin Kim, Jaewoong Cho, Gihyun Kwon

We propose the first compression approach for image-to-shape Diffusion Transformers (DiTs) that substantially reduces modelsize while preserving geometric fidelity. Despite remarkable progress in3D shape generation, large DiT-based models remain computationallyprohibitive in resource-constrained settings. Furthermore, it is difficult todirectly transfer existing diffusion model compression strategies developedfor different domains to 3D generation, and prior 3D efficiency approachesfocus primarily on inference speed rather than backbone compression. Toaddress this limitation, we build a geometry-aware compression frameworktailored to image-to-shape DiTs. Guided by the observation that 3DDiT layers exhibit non-uniform importance for geometry synthesis, weintroduce a vitality-guided framework integrating structured pruning,adaptive quantization, and targeted fine-tuning. Our method achieves upto 66% model-size reduction across state-of-the-art image-to-3D modelswhile maintaining synthesis fidelity comparable to full-sized counterparts.This highlights the potential of our framework as a plug-and-play solutionfor efficient 3D shape generation across diverse models.

ECCV 2026

UniTriSplat: A Unified 3D Gaussian Splatting Framework with Uniform Spherical Rasterization for Universal Cameras

Yipeng Zhu, Huajian Huang, Tristan Braud, Sai-Kit Yeung

Existing 3D Gaussian Splatting (3DGS) frameworks rely oncamera-speci_x001C_c rasterization, su_x001B_ering from inconsistent solid-angle sam-pling and degraded performance across heterogeneous camera models(e.g., perspective, _x001C_sheye, omnidirectional). To address this limitation,we propose UniTriSplat, a uni_x001C_ed 3DGS framework for universal camerasthat reformulates Gaussian splatting on the unit sphere via HEALPixdiscretization. Leveraging the equal-area property of HEALPix, we con-struct a spherical sampling grid aligned with the angular resolution ofinput images. We derive the forward rendering and gradient propagationof Gaussians directly in the spherical radian domain, yielding uniform op-timization behavior from narrow-FoV images to full 360-degree panora-mas. To enhance perceptual reconstruction quality, we additionally intro-duce a HEALPix-aware SSIM loss that respects spherical neighborhoodstructure. Extensive experiments across diverse camera models demon-strate that UniTriSplat consistently improves cross-camera generaliza-tion while preserving geometric _x001C_delity and rendering quality. Projectpage: https://yipengzhu0809.github.io/UniTriSplat/

ECCV 2026

UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation

Ziyi Wang, Xinshun Wang, Shuang Chen, Yang Cong, Mengyuan Liu

We present UniMotion, to our knowledge the first unifiedframework for simultaneous understanding and generation of humanmotion, natural language, and RGB images within a single architecture.Existing unified models handle only restricted modality subsets (e.g.,Motion–Text or static Pose–Image) and predominantly rely on discretetokenization, which introduces quantization errors and disrupts temporalcontinuity. UniMotion overcomes both limitations through a core princi-ple: treating motion as a first-class continuous modality on equal footingwith RGB. A novel Cross-Modal Aligned Motion VAE (CMA-VAE) andsymmetric dual-path embedders construct parallel continuous pathwaysfor Motion and RGB within a shared LLM backbone. To inject visual-semantic priors into motion representations without requiring images atinference, we propose Dual-Posterior KL Alignment (DPA), which distillsa vision-fused encoder’s richer posterior into the motion-only encoder. Toaddress the cold-start problem—where text supervision alone is too sparseto calibrate the newly introduced motion pathway—we further proposeLatent Reconstruction Alignment (LRA), a self-supervised pre-trainingstrategy that uses dense motion latents as unambiguous conditions toco-calibrate the embedder, backbone, and flow head, establishing a stablemotion-aware foundation for all downstream tasks. UniMotion achievesstate-of-the-art performance across seven tasks spanning any-to-any un-derstanding, generation, and editing among the three modalities, withespecially strong advantages on cross-modal compositional tasks.

ECCV 2026

Unifying CNNs and ViTs for Learning-Efficient and Scalable Variational AutoEncoder

Zhiying Lu, Shang Chai, Chuanbin Liu, Litong Gong, Pandeng Li, Tiezheng Ge, Hongtao Xie

Continuous variational autoencoders (VAEs) are widely usedas visual tokenizers for latent diffusion models. While CNN-based VAEsreliably capture local details, they model long-range dependencies ineffi-ciently. Vision Transformers (ViTs) provide global context, but existingViT-VAEs exhibit three practical limitations in this setting: (i) weakresolution extrapolation when trained at a single resolution, (ii) slowconvergence with suboptimal fine-grained color and texture fidelity, and(iii) inconsistent scaling gains when increasing model size. To addressthese challenges, we propose TransVAE, a hybrid CNN-ViT VAE thatcombines a shallow CNN front-end for local feature extraction with adeep Transformer backbone for global modeling. We apply minimal yetprincipled architectural modifications and systematically analyze their ef-fectiveness, including a pure Rotary Position Embedding (RoPE) strategy,a multi-stage design, and a convolutional feed-forward network. Acrossmodel sizes from 44M to 2.3B parameters, TransVAE shows learning-efficient training and predictable scaling improvements, and it supportssingle-resolution training (256×256) with robust inference at higher resolu-tions (512 × 512/1024 × 1024). Moreover, TransVAE achieves competitivereconstruction and downstream generation performance compared tostrong tokenizers, such as FLUX-VAE and VA-VAE, offering a favorabletrade-off between reconstruction fidelity and generation-friendly latents.

ECCV 2026

Tuning-free Visual Effect Transfer across Videos

Maxwell Jones, Rameen Abdal, Or Patashnik, Ruslan Salakhutdinov, Sergey Tulyakov, Jun-Yan Zhu, Kuan-Chieh Wang

We present RefVFX, a new framework that transfers complextemporal effects from a reference video onto a target video or image ina feed-forward manner. While existing methods excel at prompt-basedor keyframe-conditioned editing, they struggle with dynamic temporaleffects such as dynamic lighting changes or character transformations,which are difficult to describe via text or static conditions. Transferringa video effect is challenging, as the model must integrate the new tem-poral dynamics with the input video’s existing motion and appearance.To address this, we introduce a large-scale dataset of triplets, where eachtriplet consists of a reference effect video, an input image or video, and acorresponding output video depicting the transferred effect. Creating thisdata is non-trivial, especially the video-to-video effect triplets, which donot exist naturally. To generate these, we propose a scalable automatedpipeline that creates high-quality paired videos designed to preserve theinput’s motion and structure while transforming it based on some fixed,repeatable effect. We then augment this data with image-to-video effectsderived from LoRA adapters and code-based temporal effects generatedthrough programmatic composition. Building on our new dataset, wetrain our reference-conditioned model using recent text-to-video back-bones. Experimental results demonstrate that RefVFX produces visuallyconsistent and temporally coherent edits, generalizes across unseen effectcategories, and outperforms prompt-only baselines in both quantitativemetrics and human preference. See our website at this URL

ECCV 2026

TACO-Net: Topological Signatures Triumph in 3D Object Classification

Anirban Ghosh, Ayan Dutta

3D object classification is a crucial problem due to its significant practical relevance in many fields, including computer vision, robotics, and autonomous driving. Although deep learning methods applied to point clouds sampled on CAD models of objects and/or captured by LiDAR or RGBD cameras have achieved remarkable success in recent years, achieving high classification accuracy remains a challenging problem due to the unordered point clouds and their irregularity and noise. To this end, we propose a novel state-of-the-art (SOTA) 3D object classification technique that combines topological data analysis with various image filtration techniques to classify objects when they are represented using point clouds. We transform every point cloud into a voxelized binary 3D image to extract distinguishing topological features. Next, we train a lightweight one-dimensional Convolutional Neural Network (1D CNN) using the extracted feature set from the training dataset. Our framework, TACO-Net, sets a new state-of-the-art by achieving 99.05% and 99.52% accuracy on the widely used synthetic benchmarks ModelNet40 and ModelNet10. It also achieved one of the highest accuracies among all the from-scratch supervised learning techniques when tested on the hardest variant of the ScanObjectNN dataset. When tested with ten different kinds of corrupted ModelNet40 inputs, the proposed TACO-Net demonstrates strong resiliency overall. The code can be found in the supplementary material.

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