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ECCV 2026

UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Driving

Siyi Li, Qingwen Zhang, Ishan Khatri, Kyle Vedder, ERIC EATON, Deva Ramanan, Neehar Peri

LiDAR scene flow is the task of estimating per-point 3D mo-tion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on popular autonomous vehicle (AV) datasets, but aretypically only trained and evaluated on a single sensor. In this paper, weaim to learn general motion priors that transfer to diverse and unseenLiDAR sensors. However, prior work in LiDAR semantic segmentationand 3D object detection demonstrate that naively training on multipledatasets yields worse performance than single dataset models. Interest-ingly, we find that this conventional wisdom does not hold for motionestimation, and that state-of-the-art scene flow methods greatly benefitfrom cross-dataset training without architectural modification. We positthat low-level tasks such as motion estimation may be less sensitive tosensor configuration; indeed, our analysis shows that models trained onfast-moving objects (e.g., from highway datasets) perform well on fast-moving objects, even across different datasets. Informed by our analy-sis, we propose UniFlow, a feedforward model that unifies and trainson multiple large-scale LiDAR scene flow datasets with diverse sensorplacements and point cloud densities. Our frustratingly simple solutionestablishes a new state-of-the-art on Waymo and nuScenes, improvingover prior work by 5.1% and 35.2% respectively. Moreover, UniFlowachieves state-of-the-art accuracy on unseen datasets like TruckScenesand AEVAScenes, outperforming prior dataset-specific models by 30.1%and 22.5% respectively. See our project page for additional visuals.

ECCV 2026

Towards More Efficient Decoding for Autoregressive Vision-language-action Models

Wenxuan Song, Jiayi Chen, Pengxiang Ding, Yuxin Huang, Han Zhao, Yinchuan Li, Yingcong Chen, Donglin Wang, Haoang Li

The practical deployment of autoregressive (AR) Vision-Language-Action (VLA) models is severely constrained by inference speed bottlenecks, particularly in high-frequency and dexterous manipulation tasks. While recent studies have explored Jacobi decoding as a more efficient alternative to traditional autoregressive decoding, its practical benefits are marginal due to the lengthy iterations. To address this problem, we introduce consistency distillation to teach the model to predict multiple correct action tokens in each iteration, thereby reducing the total iterations. While the distillation brings moderate speedup, we identify that certain redundancy iterations remain a critical limitation. To tackle this, we propose an adaptive early-exit decoding strategy that moderately relaxes convergence conditions, which further improves average inference efficiency. Experimental results show that the proposed method achieves more than 4× inference acceleration across different base models while maintaining high task success rates in both simulated and real-world robot tasks. These experiments validate that our approach provides an efficient and general paradigm for accelerating multimodal decision-making in robotics. Our code and videos could be found in the supplementary files.

ECCV 2026

Towards Alias-Free 4D Gaussian Representations with Motion-Aware Filtering

Ankit Dhiman, Kunal A Kathare, Pranav Vignesh, Lokesh Boregowda, Venkatesh Babu Radhakrishnan

Novel-view synthesis of dynamic scenes, crucial for AR/VRapplications, remains a challenging problem. Recent methods adapt rep-resentations like 3D Gaussian Splatting (3DGS) and Neural RadianceFields (NeRF) for dynamic scenes by incorporating time as the fourth di-mension (4D representations). These 4D representations still suffer fromaliasing artifacts, especially when generating novel views from divergentviewpoints (zoom-in/zoom-out operations). While using 3D smoothingfilters like those proposed in Mip-Splatting might seem like a possible so-lution, they fail to account for local motion and also exhibit aliasing. Toaddress this, we propose a motion-aware 3D smoothing filter specificallydesigned for 4D representations. Our approach adapts the filter strengthbased on local motion information, effectively mitigating aliasing withoutcompromising rendering quality. This is achieved by estimating the jointdensity function of time and focal-to-depth ratio using a non-parametricestimation method. During inference, we sample from this joint distribu-tion to determine the appropriate smoothing filter. This flexible strategycan be integrated with various 4D representations. Our evaluations onstandard datasets demonstrate superior performance compared to state-of-the-art methods.

ECCV 2026

TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning

Ming Li, Jike Zhong, Shitian Zhao, Haoquan Zhang, Shaoheng Lin, Yuxiang Lai, Chen Wei, Konstantinos Psounis, Kaipeng Zhang

The frontier of visual reasoning is shifting toward models likeOpenAI o3, which can intelligently create and operate tools to trans-form images for problem-solving, also known as thinking-with-images inchain-of-thought. Yet existing benchmarks fail to fully capture this ad-vanced capability. Even Visual Search, the most common benchmark forcurrent thinking-with-images methods, tests only basic operations suchas localization and cropping, offering little insight into more complex,dynamic, and tool-dependent reasoning. We introduce TIR-Bench, acomprehensive benchmark for evaluating agentic thinking-with-imagesacross 13 diverse tasks, each requiring novel tool use for image process-ing and manipulation in chain-of-thought. We evaluate 22 multimodallarge language models (MLLMs), from leading open-sourced and propri-etary models to those with explicit tool-use augmentation. Results showthat TIR-Bench is universally challenging, and strong performance re-quires genuine thinking-with-images capabilities. Finally, we present apilot study comparing direct versus agentic fine-tuning.

ECCV 2026

The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models

Robert Welch, Emir Konuk, Kevin Smith

Vision-language models (VLMs) are increasingly deployedin high-stakes settings where reliable uncertainty quantification (UQ)is as important as predictive accuracy. Extended reasoning via chain-of-thought (CoT) prompting or reasoning-trained models has becomeubiquitous in modern VLM pipelines, yet its effect on UQ reliabilityremains poorly understood. Our results show that reasoning tends todegrade the quality of many uncertainty estimates, even when it im-proves task accuracy. We identify implicit answer conditioning as theprimary mechanism: as reasoning traces converge on a conclusion beforethe final answer is generated, token probabilities increasingly reflect con-sistency with the model’s own reasoning trace rather than uncertaintyabout correctness. In effect, the model becomes overconfident in its an-swer. In contrast, agreement-based consistency remains robust and oftenimproves under reasoning, making it a practical choice for uncertaintyestimation in reasoning-enabled VLMs.

ECCV 2026

TextDS: Parameter-Efficient Representation Alignment for Scene Text Detection under Distribution Shifts

Boyuan Chen, Zichen Dang, Chuang Yang, Lap-Pui Chau, Yi Wang

In real-world deployments, scene text detectors inevitablyface distribution shifts beyond the training distribution. Prior work of-ten depends on large-scale scene-text pretraining, yet evaluation undercross-domain changes and real-world imaging degradations remains lim-ited. We propose TextDS, an efficient framework for scene text detec-tion under distribution shifts. First, we propose a data-efficient dual-encoder design with visual foundation models, eliminating the reliance onlarge-scale scene-text pretraining. Second, we introduce Step-wise LoRAadaptation (SWLoRA), which performs progressive low-rank refinementwith a dynamic early-exit mechanism for effective feature adaptation.Third, we propose Common Subspace Fusion (CSF) to align and fuse thetwo branches in a shared subspace while retaining complementary, shift-robust information. Finally, we construct adverse-condition scene text de-tection datasets to address the gap in evaluating under imaging degrada-tion. Experiments show that TextDS achieves competitive performancein scene text detection, demonstrating robustness across domains andadverse imaging conditions with only 4.9M trainable parameters. Thecode is publicly available at https://github.com/ZChenDang/TextDS

ECCV 2026

TDSR-VLA: Transition-aware Denoising Sequence Representations for Vision-Language-Action

Dong-Woo Kim, KEUNHO SONG, Seungmin Lee, Hwanhee Ju, Eun Cha, Daekyum Kim

Vision–language–action (VLA) policies often become brit-tle under deployment distribution shift when only limited target-domaindemonstrations are available. Subgoal-image planning addresses this is-sue by providing an explicit visual target. However, the target imagespecifies what future scene to reach, not how the robot should transitiontoward it. We propose TDSR-VLA, a VLA framework that reuses condi-tioning states from a diffusion-based Vision Planner to guide action gen-eration. The Vision Planner predicts a subgoal image and provides tworepresentations: a Sequence Representation (SR) grounded in the currentobservation and an Updated Sequence Representation (USR) refined dur-ing denoising steps. A flow-matching Action Expert generates continuousaction chunks by injecting these representations through layer-wise pre-fix key–value conditioning. On real-world tasks, TDSR-VLA achieves anaverage OpenArm score of 0.83, outperforming GR00T N1.5 (0.74), π0(0.71), and SmolVLA (0.58), with the largest gains on object-cooperativetasks (0.88 vs. 0.70). On LIBERO, TDSR-VLA reaches 82.0% on Goaland 56.0% on Long without robot-demonstration pretraining, outper-forming OpenVLA on both suites and π0 on Long. Ablation studies showthat planner-side conditioning states provide action-relevant transitioncues, improving long-horizon manipulation under limited training data.

ECCV 2026

TaskTok: Delving into Task Tokens for Task-driven Image Restoration

Hongjae Lee, Sojung Kang, Jaeseong Yu, Seung-Won Jung

While traditional image restoration focuses on perceptualquality, Task-Driven Image Restoration (TDIR) aims to maximize theperformance of downstream high-level vision tasks. Recent approachesleveraging generative priors have shown promise for TDIR; however,they typically suffer from computational inefficiency and potential se-mantic alteration by indiscriminately updating all latent tokens. In thispaper, we posit that not all visual information is equally important formachine perception. Through an analysis of the latent token space, weobserve that task-relevant cues are unevenly distributed across the to-ken sequence, exhibiting index-wise specialization. This suggests thatselectively refining a subset of tokens can be sufficient for task-drivenobjectives. Leveraging this insight, we propose TaskTok, a novel frame-work that selectively restores only task-relevant tokens via a learnabletoken switch and a lightweight token refinement module. Extensive ex-periments across image classification, semantic segmentation, and objectdetection demonstrate that TaskTok significantly enhances task perfor-mance with high computational efficiency. The source code is availableat our project page https://github.com/jimmy9704/TaskTok.

ECCV 2026

StyleFusion360: View-Consistent Head Stylization via Adaptive Style Modulation

Furkan Guzelant, Arda Goktogan, Tarık Kaya, Aysegul Dundar

3D head stylization enables expressive reimagining of hu-man faces for creative visual experiences in digital media. Existing 3D-aware methods often require computationally intensive optimization orper-style fine-tuning, limiting flexibility and user control. To overcomethese challenges, we introduce StyleFusion360, a diffusion-based frame-work for multi-view consistent, identity-preserving 3D head stylizationfrom a single style reference image, without per-style training. Our ap-proach enhances the Style Fusion Attention mechanism with a style-conditioned key modulation mechanism that aligns content and stylerepresentations for fine-grained and controllable stylization. We furtherprovide a user-controllable slider for adjusting stylization intensity. Inaddition, StyleFusion360 supports local multi-edit stylization, enablingtargeted edits such as modifying hair or eyes independently. Extensiveexperiments on FFHQ and RenderMe360 demonstrate that StyleFu-sion360 produces high-quality, controllable, and visually compelling styl-izations, outperforming state-of-the-art GAN- and diffusion-based meth-ods across diverse style domains. Code is available at: https://github.com/furkanguzelant/StyleFusion360

ECCV 2026

Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Classification

Jeeyung Kim, Erfan Esmaeili, Qiang Qiu

When labeled data are scarce, off-the-shelf diffusion modelscan augment training sets for few-shot medical image classification, butnot all generated samples are equally useful for the downstream task. Ex-isting approaches largely improve synthetic data by increasing realism,diversity, or domain adaptation, while overlooking a more fundamen-tal question: how should sample usefulness for classification be measuredand optimized? We address this with Class-Contrastive Influence (C2I),a criterion that quantifies a sample’s usefulness through its gradient-based influence on the classifier. We find that effective samples exhibita strong C2I gap: their loss gradients align with validation gradientsfrom the same class and oppose those from other classes. Our anal-ysis further suggests that such high-C2I samples are hard, boundary-proximal examples that help refine the decision boundary and improverobustness. Building on this insight, we fine-tune diffusion models withreinforcement learning using a C2I-based reward to steer generation to-ward class-informative samples. Across several few-shot medical imagingbenchmarks, C2I-guided generation improves downstream accuracy androbustness over diffusion-based augmentation baselines, showing thatsynthetic augmentation is most effective when guided by task usefulnessrather than image quality alone.

ECCV 2026

Slim-DETR: Real-Time Tiny Object Detection with Efficient Interaction and Gaussian Query

Tong Wu, Jiahao Zhang, Juntao Guan, Lai Rui

Tiny object detection is critical for applications ranging from drone-based scene analysis to remote sensing. However, existing DETR frameworks suffer from inefficient encoder architectures and a severe query-object misalignment for tiny targets, hindering their deployment in real-time scenarios. To overcome these limitations, we propose SlimDETR, a novel framework tailored for real-time tiny object detection. Central to our approach is an Efficient Interaction (EI) encoder, which employs a gather-and-inject mechanism to aggregate global semantics. Uniquely, the EI encoder restricts semantic attention interactions exclusively to extracted foreground tokens, thereby significantly alleviating the computational overhead of conventional Transformer-based encoders. Furthermore, we introduce a Gaussian Target Guided (GTG) query selection strategy that leverages the Gaussian spatial distribution of targets to dynamically concentrate high-quality queries on regions containing tiny objects. Extensive experiments demonstrate that Slim-DETR achieves state-of-the-art performance, yielding 33.9% AP on VisDrone and 33.6% AP on AI-TOD-V2. Notably, it maintains real-time inference speeds across platforms, achieving a latency of 24.4 ms on an NVIDIA RTX 3090 GPU and 25.1 ms (40 FPS) on the embedded Jetson AGX Orin.

ECCV 2026

Show Me Examples: Inferring Visual Concepts from Image Sets

Nick Stracke, Kolja Bauer, Stefan Andreas Baumann, Miguel Angel Bautista, Joshua Susskind, Bjorn Ommer

Vision-language models (VLMs) can follow complex textualinstructions, yet they struggle to reason from purely visual context. Inparticular, current models fail to infer shared concepts from sets of exam-ple images and apply them to new inputs. We introduce Visual ConceptInference from Sets (VICIS), a task that evaluates this capability. Givena small context set of images sharing a concept and a query image, themodel must generate new images that preserve the context-defined conceptwhile remaining consistent with the query. We show that state-of-the-artVLMs perform poorly on this task, often ignoring the visual contextor defaulting to biased generations. To address this gap, we propose atraining framework and architecture that learn to infer visual conceptsfrom image sets and extract concept-specific embeddings from queries.Experiments on synthetic data and large-scale ImageNet/WordNet datashow that our model generates more accurate and diverse outputs andgeneralizes to unseen concepts and modalities such as sketches.

ECCV 2026

Shared LoRA Subspaces for almost Strict Continual Learning

Prakhar Kaushik, Ankit Vaidya, Shravan Sunil Chaudhari, Rama Chellappa, Alan Yuille

Adapting large pretrained models to new tasks e!ciently andcontinually is crucial for real-world deployment but remains challengingdue to catastrophic forgetting and the high cost of retraining. Whileparameter-e!cient tuning methods like low-rank adaptation (LoRA) re-duce computational demands, they lack mechanisms for strict contin-ual learning and knowledge integration, without data replay or multi-adapters or MoE. We propose Share, a novel approach to parameter-e!cient continual finetuning that learns and dynamically updates a sin-gle, shared low-rank subspace, enabling seamless adaptation across multi-ple tasks and modalities. Share constructs a foundational subspace thatextracts core knowledge from past tasks and incrementally integratesnew information by identifying essential subspace directions. Knowledgefrom each new task is incorporated into this evolving subspace, enablingforward knowledge transfer while minimizing catastrophic interference.This approach achieves up to 100× reduction in trainable parametersand 281× memory savings over traditional LoRA methods, maintain-ing performance comparable to jointly trained models. A single Sharemodel can replace hundreds of task-specific LoRA adapters, support-ing scalable, asynchronous continual learning. Experiments across imageclassification, natural language understanding, 3D pose estimation, andtext-to-image generation validate its e"ectiveness, making Share a prac-tical and scalable solution for lifelong learning in large-scale AI systems.

ECCV 2026

Scaling Multi-Reference Image Generation with Dynamic Reward Optimization

Wenwang Huang, Yusen Fu, Mengfei Huang, Junjie Wang, Yulin Li, Gan Liu, Jing Cai, Yancheng He, Zhuotao Tian

While personalized image generation has achieved remark-able progress, multi-reference image generation (MRIG) remains a chal-lenging task. Most existing benchmarks fail to adequately evaluate com-plex MRIG scenarios, hindering further progress in this area. To bet-ter assess model performance on complex MRIG tasks, we introduceOmniRef-Bench, a benchmark that covers complex combinations of ref-erence image types and a large number of reference images. Evaluationson OmniRef-Bench show that mainstream open-source models strugglein complex MRIG scenarios, and their performance deteriorates signifi-cantly as the number of mixed-type reference images increases. To ad-dress this issue, we propose DyRef, a two-stage training framework.In the first stage, supervised fine-tuning equips the model with the ba-sic capability to handle complex MRIG tasks. In the second stage, weintroduce Difficulty-aware Advantage Reweighting (DAR) and Discrim-inative Reward Scaling (DRS). DAR dynamically adjusts the optimiza-tion objective to improve performance when handling a large numberof mixed-type reference images. DRS enlarges intra-group reward differ-ences for more effective policy optimization. Experiments demonstratethat DyRef significantly improves the performance of open-source modelson OmniRef-Bench and single-image editing benchmarks, demonstratingthe effectiveness and generalization capability of our approach. Our codeis available at https://github.com/Weistrass/DyRef.

ECCV 2026

S3-Prune: Stability-Aware Token Budgeting for Long-Form Video-Language Models

Gilha lee, Seungil Lee, Hyun Kim

The deployment of long-form video-language models (Video-VLMs) is fundamentally constrained by the prohibitive scaling of atten-tion complexity and KV-cache overhead as temporal and spatial res-olutions increase. Existing pruning methods rely on local frame-levelheuristics, which fail to capture shifting information density and oftenresult in cumulative budget misallocation over long horizons. To addressthese challenges, we propose S3 -prune, a stability-aware token budgetingframework that jointly models spatio-temporal demand while ensuringconsistent allocation over time. S3 -prune characterizes token demand byintegrating spatial uncertainty (SU) from patch-level embedding shiftsand segment transition (ST) from inter-segment semantic variations. Tomitigate temporal instability, we introduce stability accumulation (SA)via a Kalman-filtered latent estimator, which smoothens stochastic spikesand provides uncertainty-aware margins for adaptive budgeting. Guidedby these stabilized budgets, S3 -prune executes a hierarchical two-stageselection process. Extensive evaluations across seven benchmarks andvarious backbones demonstrate that S3 -prune significantly advances theefficiency-accuracy frontier, reducing inference latency while remainingrobust even under aggressive token retention settings.

ECCV 2026

S2Gest: Split-Scan State Space Models for Dynamic Hand Gesture Recognition

Kefan Chen, Yong Gu, Bo Li, Longjie Huang, Jiajun Zhang

Dynamic Hand Gesture Recognition is essential for naturalhuman-computer interaction but faces challenges in continuous local ex-ecution on consumer-grade hardware due to strict computational andlatency constraints. Existing solutions fail to reconcile the high MemoryAccess Cost (MAC) and dynamic footprint of Video Transformers with thelimited temporal modeling capacity of lightweight CNNs. To address theselimitations, we propose the Split-Scan Gesture Network (S2Gest), whichrethinks temporal modeling by leveraging continuous state-space evolutionto represent discrete sequential inputs. We introduce the Split-Scan Block(S2-Block), employing a novel tube-wise scanning strategy that partitionsfeature channels to process opposing temporal directions simultaneously.By structurally partitioning feature channels to capture complementaryforward and backward dynamics, this design achieves global bidirectionalmodeling with linear-time complexity and without introducing additionalparameters, effectively overcoming the causal unidirectionality inherent instandard State Space Models (SSMs). Evaluated across three benchmarks,the scalable S2Gest model family establishes a new state-of-the-art forlightweight architectures, matching the performance of large-scale base-lines with a sub-3M parameter footprint while consistently exceeding 100clips/s on consumer-grade GPUs. Code and weights are publicly availableat https://github.com/Chen-Ke-Fan/S2Gest.

ECCV 2026

Training-free Discriminative Patch Mining for Robust Few-Shot Recognition with CLIP

Zhenzhang Ye, Duolikun Danier, Bo Zhao, Hakan Bilen

Few-shot classification performance of Contrastive Language-Image Pre-training (CLIP) varies widely across datasets, especially whenclass names provide weak or ambiguous semantic priors. This issue is alle-viated in vision-only fine-grained methods, as they identify discriminativelocal features but require training on the full dataset. We thus introduce atraining-free approach that integrates discriminative patches into CLIPto reduce reliance on textual prompts. Our method identifies patcheswith high intra-class consistency and low inter-class ambiguity, forminga Class-Discriminative Patch Set (CDPS) for each category. Using CDPS,we enhance the recognition ability of CLIP through a hybrid classifiercombining global image-text alignment with local patch-based similarity.Extensive experiments on diverse benchmarks show that CDPS injectsfine-grained discriminatory power into CLIP and yields more robust few-shot recognition performance against ambiguous class names.

ECCV 2026

RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement

Jiyao Wang, Qingyong Hu, Duoxun Tang, Xiao Yang, Kaishun Wu, Jiangbo Yu

Video-based remote physiological measurement (RPM) ishighly accessible but remains fragile under varying illumination, skintones, and motion. Radio frequency (RF) radar is largely invariant to il-lumination and appearance, providing complementary cardio-respiratorymicro-motion cues; however, requiring radar at inference is often imprac-tical due to its limited ubiquity and deployment overhead. We proposeRPM-Distill, a physiology-guided cross-modal distillation frameworkthat leverages synchronized radar only during training while retainingvideo-only inference. Our key observation is that although RGB and RFwaveforms differ in sensing physics and time-domain morphology, theyshare similar latent periodic rhythm in the frequency domain. We thusdistill physiology-structured spectral evidence to improve robustness, vialosses that (i) anchor the fundamental peak, (ii) match the off-peak back-ground distribution, and (iii) preserve spectral morphology and sharp-ness. To avoid negative transfer under sample-level teacher quality andalignment uncertainty, a spectral policy network predicts sample-leveldistillation gates and component weights from the student–teacher spec-tral relation map, learned with a meta bilevel objective on a small labeledvalidation split. Through extensive experiments in challenging conditionsand cross-dataset settings, RPM-Distill reduces MAE by up to 81% andimproves correlation by up to 21% over the best video-only baseline.Code is at https://github.com/WJULYW/RPM-Distill.

ECCV 2026

Rethinking Real-World MRI Denoising: Learning from Physical Noise

Sebastian Rassmann, David Kügler, Sascha Brunheim, Philipp Ehses, Martin Reuter

Magnetic resonance imaging (MRI) inherently suffers fromnoise, which limits downstream medical analyses. In MRI, noise-freeimages are unobtainable; therefore, existing denoising approaches for-mulate surrogate training objectives, compromising between preservingdetail and concealing noise, causing domain shifts or incomplete de-noising. To enable denoiser training directly on unmodified, noisy im-ages, we exploit repeated acquisitions. This naturally constitutes a phys-ical Noise2Noise (pN2N) setting. For unrepeated data, we introducea diffusion-based re-noiser that synthesizes noisy image pairs, extend-ing pN2N to Renoise2Noise (ReN2N). Furthermore, we demonstratethat ReN2N improves generalization to unseen datasets. Additionally,we propose to combine pN2N or ReN2N with optional guidance fromco-acquired contrast, yielding four versions of our novel denoising frame-work: YADO (You Accurately Denoise real Observations). Across 14 testconditions, YADO consistently outperforms 17 state-of-the-art baselines,matching the quality of physically noise-suppressed images obtained viabrute-force averaging of independent acquisitions. YADO thus estab-lishes practical denoising for real-world acquisition settings.

ECCV 2026

ReGen3D: Generalizable Unified Representation Learning for 3D Understanding

Haotian Zhang, Jincen Jiang, Yuhang Li, Jian Jun Zhang, Meili Wang, Jian Chang

Existing 3D understanding models are typically built on afixed geometric representation and often fail when the representationformat changes. Unlike domain generalization, which studies distribu-tion shift under a fixed input structure, we focus on representation shift,where identical 3D semantics are expressed through heterogeneous geo-metric encodings such as points, voxels, meshes, and 3D Gaussian prim-itives. As 3D representations continue to evolve beyond a predefinedset, this challenge is becoming increasingly important. We formalize thisproblem as Representation Generalization (RG), a new setting for unified3D learning. RG requires a model to share knowledge across representa-tions during training, remain effective when only a single representationis available at test time, and extend efficiently to unseen or newly emerg-ing representations. To this end, we propose ReGen3D, a unified frame-work with three modules: Unified Representation Tokenization (URT),which maps heterogeneous representations into a shared token space;Shared Memory Interaction (SMI), which enables cross-representationlearning during training while supporting inference from any single avail-able representation; and Efficient Representation Adapter (ERA), whichsupports lightweight adaptation to unseen representations without modi-fying the shared backbone. We further establish RepShift-3D for system-atic evaluation under the RG setting. Extensive experiments on classi-fication, segmentation, and reconstruction show that ReGen3D achievesstrong and robust performance across diverse representation scenarios.

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