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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents

Kenta Watanabe, Atsuyuki Miyai, Mizuki Takenawa, Kiyoharu Aizawa, Toshihiko Yamasaki

We present 360CityArena, a benchmark for evaluating theurban exploration capabilities of Embodied Agents within a photorealis-tic environment constructed from 360° videos. Existing outdoor bench-marks either lack sufficient photorealism or complexity, resulting in aconsiderable gap from real-world urban environments. 360CityArena isbuilt on a realistic reconstruction of the Akihabara district in Tokyo,Japan, using 602 360° video segments covering 85 streets, and consistsof 175 meticulously human-crafted tasks. It encompasses three task cat-egories: Environment Understanding, Path Reasoning, and Spatial Rea-soning, covering fundamental abilities required for urban exploration,such as localization, landmark search, path planning, and relational spa-tial reasoning, thereby enabling comprehensive evaluation in realisticurban scenes. Our evaluation using state-of-the-art LMM-based agentsshows that even the strongest model, Gemini-2.5 Flash, performs far be-low human level (human: 77.3% vs. Gemini-2.5 Flash: 17.1%), revealingsubstantial challenges that remain in city-scale embodied navigation andreasoning. 360CityArena provides a necessary and challenging testbed forphotorealistic urban-district navigation and spatial reasoning.

ECCV 2026

WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens

Yiwei Guo, Shaobin Zhuang, Zhipeng Huang, Canmiao Fu, Chen Li, Jing Lyu, Yali Wang

Building a unified visual tokenizer is essential for bridgingthe gap between visual understanding and generation. Yet existing ap-proaches struggle with the inherent conflict between these tasks, as asingle token space is forced to support both high-level semantic abstrac-tion and low-level pixel reconstruction. We propose WinTok, a concisehybrid tokenizer that achieves a win-win performance by explicitly de-coupling the two objectives. WinTok supplements pixel tokens with aset of learnable semantic tokens, effectively mitigating cross-task inter-ference without incurring the computational overhead of dual tokenizers.To further enhance understanding capability, we introduce an asymmet-ric token distillation mechanism: the semantic tokens are guided by pre-trained semantic embeddings from any visual foundation model, enablingthem to inherit strong discriminative power while maintaining flexibility.Across 10 challenging benchmarks, WinTok delivers consistent improve-ments in reconstruction, understanding, and generation. Trained on only50M open-source data, WinTok surpasses the strong baseline UniTok by11.2% in classification accuracy and achieves a competitive reconstruc-tion rFID of 0.41, despite using substantially less training data. Code isreleased at https://github.com/WeChatCV/WinTok.

ECCV 2026

When 3D Gaussian Splatting Recovers Real Surfaces

Songhe Wang, David Miller

When does 3D Gaussian Splatting (3DGS) recover the truescene surface rather than just overfitting view-dependent appearance? Weanswer this by developing a mathematical framework—based on a first-hitrendering abstraction—that cleanly isolates geometry from appearance.We prove that geometric misalignment forcefully converts spatial texturesinto high-frequency angular signals via parallax. This establishes a strictidentifiability window: if angular capacity is bounded, surface-consistentsolutions are mathematically preferred; if unrestricted, the same imagescan be perfectly explained by an incorrect, opaque “billboard” geometry.Experiments on synthetic stress tests confirm this prediction, showingbillboard failures emerge precisely at high angular capacities. Conversely,in the real-world datasets we evaluate under standard capture proto-cols, reconstructions remain surface-consistent even at high SH degrees,which is consistent with the prediction that rich spatial texture can pushbillboard solutions outside the tested angular-capacity range.

ECCV 2026

VisReflect: Latent Visual Reflection for Fine-Grained Perception in Long Visual Context

Xiaoqian Shen, Mohamed Elhoseiny

Large Vision Language Models (LVLMs) have achieved re-markable success on vision–language tasks, yet fine-grained perceptionover high-resolution images and long-context videos remains challenging.As the number of visual tokens increases, the visual attention sink phe-nomenon becomes increasingly severe, causing irrelevant tokens to absorba disproportionate amount of attention mass. Recent approaches attemptto mitigate this issue by explicitly predicting bounding boxes or temporalspans and re-encoding the cropped visual regions. Such methods dependon unreliable numeric localization in the discrete token space and in-cur significant computational overhead due to additional forward passes.In this work, we propose VisReflect, a simple yet effective frameworkthat improves fine-grained perception in long visual contexts through la-tent visual reflection. Instead of decoding intermediate predictions intodiscrete tokens, the model generates continuous visual reflection thatrepresents question-relevant visual features in the latent space. Thesereflections selectively emphasize salient regions or frames, guiding at-tention towards relevant visual tokens within a single forward pass. Weconduct comprehensive evaluations on challenging high-resolution im-age benchmarks, including BLINK, V∗ , and HRBench-4K/8K, as wellas video understanding benchmarks such as MVBench, VideoMME, andMLVU. Our method consistently improves over strong baselines, achiev-ing gains of 4.1% on image benchmarks and 1.8% on video benchmarks.Compared with zooming-based methods, our model achieves compara-ble performance while reducing inference time by roughly 44% on videounderstanding.

ECCV 2026

VisCritic: Visual State Comparison as Process Reward for GUI Agents

Jiachen Qian

GUI agents powered by vision-language models show strongpotential for automating digital tasks, yet frequently fail in long-horizonscenarios due to the absence of step-level verification. Existing process re-ward models verify actions through textual reasoning alone, missing thevisual nature of GUI state changes. We introduce VisCritic, a visualprocess reward framework that verifies agent actions by directly com-paring pre-action and post-action screenshots in visual feature space.VisCritic employs a Siamese vision transformer to extract change-awarerepresentations, coupled with an Action-Aware Critic Head that jointlyevaluates action success, task progress, and error type. A critic-trainingdata construction pipeline generates weakly supervised samples from ex-isting trajectories without additional human labels for critic training.Experiments and offline analyses across five benchmarks demonstratethat VisCritic serves as a plug-and-play enhancement for diverse GUIagents, generally improving benchmark metrics while providing visualdiagnostic cues.

ECCV 2026

UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Dataset

Yi Wang, Fan Wang, Prabin Gyawali, Ziyang Xu, Anna Klimkowska, Yixiong Jing, Wanru Yang, Filip Biljecki, Christoph Holst, Benjamin Busam, Brian Sheil, Olaf Wysocki

Globally consistent semantic digital twins require centimeter-accurate and geographically transferable 3D facade segmentation. How-ever, progress in facade parsing is limited by the lack of large-scale, stan-dardized benchmarks for evaluating cross-domain and cross-country gen-eralization. Existing datasets are geographically narrow, sensor-specific,semantically inconsistent, or insufficiently precise. We introduce Un-derOneFacade, the largest cross-continental 3D facade benchmark todate, comprising centimeter-accurate point clouds with hierarchical, har-monized, and architecturally grounded semantic labels totaling 2.7 bil-lion annotated points. Through a systematic evaluation of representativepoint-, graph- and transformer-based architectures, we show that cur-rent methods struggle to recognize fine-grained architectural elementsand degrade significantly across geographic regions, with the best mod-els achieving only up to 33 IoU on the fine-grained LoFG3 benchmark.By combining geometric precision with standardized semantics at un-precedented scale, UnderOneFacade establishes a rigorous benchmarkfor developing robust and transferable 3D segmentation models for andbeyond facade understanding. The dataset, evaluation scripts, and pre-trained models are available here: https://jiangyuanwangyi.github.io/UnderOneFacade_official/

ECCV 2026

Towards Scalable Pre-training of Visual Tokenizers for Generation

Jingfeng Yao, Yuda Song, Yucong Zhou, Xinggang Wang

The quality of the latent space in visual tokenizers (e.g.,VAEs) is crucial for modern generative models. However, the standardreconstruction-based training paradigm produces a latent space that isbiased towards low-level information, leading to a foundational flaw: bet-ter pixel-level reconstruction accuracy does not lead to higher-qualitygeneration. This implies that pouring extensive compute into visual tok-enizer pre-training translates poorly to improved performance in gener-ation. We identify this as the “pre-training scaling problem” and suggesta necessary shift: to be effective for generation, a latent space must con-cisely represent high-level semantics. We present VTP, a unified visualtokenizer pre-training framework, pioneering the joint optimization ofimage-text contrastive, self-supervised, and reconstruction losses. Ourstudy reveals that perception-oriented tokenizer pre-training un-locks a new scaling law for generation, where generative perfor-mance scales effectively with compute, parameters, and data allocatedto the pre-training of the visual tokenizer. Our large-scale pre-trainingexperiments demonstrate the following results: (1) Without modifyingDiT training specs and FLOPs, solely scaling VTP pre-training con-sistently achieves gains in both ImageNet class-conditional and LAIONtext-to-image generation, while conventional autoencoders stagnate veryearly at 1/10 of the FLOPs. (2) VTP achieves 0.36 rFID while simultane-ously delivering 78.2% zero-shot accuracy and 85.7% linear probing accu-racy, surpassing prior unified tokenizers such as VILA-U and UniTok. (3)Furthermore, the VTP-based diffusion model exhibits exceptionally fastconvergence—reaching 2.03 gFID in only 80 epochs without guidancetricks, outperforming previous methods like VA-VAE and RAE—and ul-timately scales to achieve a remarkable 1.11 gFID on ImageNet 256×256generation. Our code and models are publicly available at VTP.

ECCV 2026

ToDRE: Effective Visual Token Pruning via Token Diversity and Task Relevance

Duo Li, Zuhao Yang, Xiaoqin Zhang, Ling Shao, Shijian Lu

Visual token pruning, which aims to compress and pruneredundant visual tokens, plays a critical role in efficient inference withlarge vision-language models (LVLMs). However, existing methods failto disentangle intra-modal visual redundancy from cross-modal redun-dancy between vision and language. We show that visual token diversityand task-specific token relevance are two crucial yet orthogonal factorsthat complement each other in conveying useful information and shouldtherefore be treated separately for more effective visual token pruning.Building upon this insight, we design ToDRE, a two-stage and training-free framework that incorporates Token Diversity and task RElevancefor effective token compression and efficient LVLM inference. Instead ofpruning redundant tokens, we introduce a greedy max-sum diversificationalgorithm that selects and retains a subset of diverse and representativevisual tokens after the vision encoder. On top of that, ToDRE leveragesan “information migration” mechanism to eliminate task-irrelevant visualtokens within certain decoder layers of the large language model (LLM),further improving token pruning and LVLM inference. Extensive exper-iments show that ToDRE prunes 90% of visual tokens after the visionencoder as well as all visual tokens in certain LLM decoder layers, leadingto a 2.6× speed-up in total inference time while maintaining 95.0% modelperformance plus excellent model compatibility.

ECCV 2026

There and Back Again: A Flexible-Frame Transformer for Multi-Exposure Fusion

Lishen Qu, Yao Liu, Shihao Zhou, Jie Liang, Hui Zeng, Lei Zhang, Jufeng Yang

Multi-exposure fusion (MEF) brings the dynamic range ofconventional cameras closer to that of human vision, producing imageswith rich scene content. Given the large variability in scene luminance,exposure strategies often require different numbers of frames to capturethe full radiance range faithfully. However, conventional MEF techniquesare typically designed for a fixed number of inputs, forcing deploymentsystems to maintain separate models for different frame-count require-ments, which undermines deployment efficiency. To address this limita-tion, we propose FreeMEF, the first flexible-frame transformer for MEFthat seamlessly accommodates varying numbers of input exposures with-out retraining or architectural changes. The proposed approach consistsof two key modules. First, we introduce a recurrent state space mod-ule (RSSM) that sequentially fuses features from arbitrary sequencesvia adaptive alignment and state-space recurrent modeling, thereby pro-viding global information guidance for the subsequent restoration. Sec-ond, we devise a global feature guided block (GFGB) incorporating anextremity-aware hybrid attention (EAHA) and an affine-injection feed-forward network (AFFN), which effectively resolves the similarity para-dox while simultaneously optimizing contrast and brightness regulation.Extensive experiments on three benchmark datasets demonstrate the ef-fectiveness of our method, which performs favorably against state-of-the-art methods both quantitatively and qualitatively. The code is availableat https://github.com/qulishen/FreeMEF.

ECCV 2026

Structured Redundancy Modeling for Efficient Visual Token Pruning in High-Resolution MLLMs

Jouwon Song, Woohyeong Kim, Kyeongbo Kong

Recent high-resolution Multimodal Large Language Models(MLLMs) generate thousands of visual tokens per input, leading to avisual token explosion that introduces severe latency bottlenecks. Whiletoken pruning mitigates this issue, state-of-the-art subset-optimizationmethods typically rely on iterative subset construction to jointly cap-ture visual diversity and instruction relevance. As visual token countsscale, this sequential dependency introduces significant selection over-head, severely limiting the translation of theoretical FLOPs reductionsinto actual wall-clock speedups. To address this limitation, we proposeSingle-Forward Pruner (SFPruner), a structural reformulation of visualtoken pruning that embeds redundancy control directly into the scor-ing space, bypassing the need for iterative combinatorial optimization.Our non-iterative framework achieves redundancy-aware importance se-lection in a single forward pass through two complementary mechanisms.First, to attenuate redundancy at the covariance level, we introduce asemantics-guided ridge leverage scheme. By integrating instruction rel-evance and visual saliency, this mechanism suppresses dominant covari-ance directions and mitigates representation bias. Second, ranking-baseddirectional masking resolves residual overlap through asymmetric sim-ilarity competition, where higher-scoring tokens explicitly suppress re-dundant lower-scoring alternatives via parallel tensor operations. Exten-sive evaluations demonstrate that our approach maintains stable selec-tion costs, reducing the token selection process by up to 110 ms (from112.4 ms to just 2.5 ms at 512 tokens in Qwen2.5-VL). This structuralefficiency successfully translates theoretical token reductions into tangi-ble inference speedups while preserving highly competitive performanceagainst state-of-the-art techniques under aggressive compression.

ECCV 2026

Streaming Dense Voxel Representations for 3D Occupancy Prediction

Seokha Moon, Janghyun Baek, Yujin Jeong, Daewon Chae, Giseop Kim, Jungbeom Lee, Jinkyu Kim, Sunwook Choi

In this paper, we explore dense voxel streaming for accurateand efficient 3D occupancy prediction. While dense voxel representa-tions offer fine-grained spatial details and streaming paradigm enablesefficient temporal processing, naively combining the two introduces keychallenges: (i) warping-induced distortions caused by interpolation usedfor temporal alignment, and (ii) degraded dynamic object representa-tions due to motion misalignment and detail loss in image-to-voxel pro-jection. To address these, we propose StreamOcc, a novel frameworkthat utilizes two aggregation strategies. Specifically, it first refines prop-agated voxel features to reduce warping artifacts before temporal accu-mulation, and then selectively injects instance-level query features en-coding dynamic-object semantics into the corresponding occupied voxelregions, preserving temporally consistent modeling while strengtheningdynamic object representations. Unlocking effective dense voxel stream-ing, StreamOcc achieves state-of-the-art performance on SurroundOcc-benchmark and Occ3D-nuScenes under real-time constraints, outper-forming the prior best methods by +1.3/2.5 and +1.5/2.0 in (over-all/dynamic object) mIoU, respectively, while running at 83.3 msper frame with only 2.8 GB of memory. The project page is available athttps://moonseokha.github.io/StreamOcc/.

ECCV 2026

SplatCtrlA: Generalizable Single Image to Fully Controllable 3D Avatar

Jun Xiang, Yudong Guo, Boyang Guo, Yancheng Yuan, Juyong Zhang

Expressive human avatar creation from a single image ishighly challenging due to the inherently ill-posed nature of the prob-lem, as well as the complexities of appearance preservation and dynamichuman modeling. To address these challenges, this work presents a com-prehensive pipeline for training a large feed-forward model that efficientlygenerates fully controllable 3D avatars from single-image. Due to thescarcity of consistent training data, we construct a large-scale 3D Gaus-sian avatar dataset to support model training. To better recover appear-ance details, we propose an input-aware decoding scheme that fully lever-ages information from the input image. Furthermore, to achieve compre-hensive full-body control, we introduce a Gaussian blending–based facialenhancement module and apply Gaussian geometric constraints to sta-bilize expressive avatar generation. Extensive experiments demonstratethat our method enables one-shot reconstruction of photorealistic avatarswith whole-body control, outperforming existing works in terms of hu-man dynamic and appearance details.

ECCV 2026

Semantic-Geometric Dual Compression: Training-Free Visual Token Reduction for Ultra-High-Resolution Remote Sensing Understanding

Yueying Li, Fengxiang Wang, Yan Li, Mingshuo Chen, Mengying Zhao, Long Lan

Multimodal Large Language Models (MLLMs) have demon-strated immense potential in Earth observation. However, the massivevisual tokens generated when processing Ultra-High-Resolution (UHR) im-agery introduce prohibitive computational overhead, severely bottleneck-ing their inference efficiency. Existing visual token compression methodspredominantly adopt static and uniform compression strategies, neglectingthe inherent Semantic–Geometric Duality in remote sensing interpretationtasks. Specifically, object semantic tasks focus on the abstract seman-tics of objects and benefit from aggressive background pruning, whereasscene geometric tasks critically rely on the integrity of spatial topol-ogy. To address this challenge, we propose DualComp, a task-adaptivedual-stream token compression framework. Dynamically guided by alightweight pre-trained router, DualComp decouples feature processinginto two dedicated pathways. In the object semantic stream, the Spatially-Contiguous Semantic Aggregator (SCSA) utilizes size-adaptive clusteringto aggregates redundant background while protecting small object. Inthe scene geometric stream, the Instruction-Guided Structure Recoverer(IGSR) introduces a greedy path-tracing topology completion mechanismto reconstruct spatial skeletons. Experiments on the UHR remote sens-ing benchmark XLRS-Bench demonstrate that DualComp accomplisheshigh-fidelity remote sensing interpretation at an exceptionally low com-putational cost, achieving simultaneous improvements in both efficiencyand accuracy.

ECCV 2026

Self-supervised Garment Dynamics with Persistent Wrinkles

Xiaoyuan Yang, Deshan Gong, Taku Komura, He Wang

Self-supervised neural garment simulation has become pop-ular due to its computational efficiency, good visual realism, and noreliance on training data. However, existing methods greatly simplifythe mechanical properties of fabrics, ignoring persistent wrinkles causedby plasticity. Although this simplification allows for modeling of purelyelastic material and simple training via energy minimization, the lackof believable wrinkles adversely affects the visual realism. Therefore, weintroduce the first self-supervised neural garment simulator that explic-itly models persistent wrinkles. This is accomplished through a novelphysics-inspired loss function, which turns learning into a moving energyminimization problem to mimic plasticity. However, this requires learn-ing to use a changing loss function, which causes difficulties in trainingbecause the loss function changes during optimization. To this end, wepropose a new physics-inspired curriculum learning scheme where thetarget material for learning gradually changes from pure elasticity toelasto-plasticity, allowing the loss function and the learnable parametersto jointly converge. Through a comprehensive evaluation, we show thatfor the first time, self-supervised learning models can generate naturalpersistent wrinkles, outperforming existing methods on a variety of gar-ments, body shapes, and body motions, according to a range of metrics.Our code is publicly available at https://github.com/realcrane/EPNet

ECCV 2026

SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation

Haidong Cao, Wenjun Cao, Quanhao Li, Sicheng Xie, Zhiying Du, Jiaqi Leng, Zuxuan Wu, Yu-Gang Jiang

Imitation learning enables robots to acquire manipulationskills from demonstrations by mapping observations to actions. Existingapproaches predict either short-horizon continuous action sequences ordiscrete keyposes. However, continuous prediction methods suffer fromcompounding errors due to short prediction horizons and struggle withmulti-modal action distributions, whereas keypose-based methods ne-cessitate an external planner, constraining real-time applicability. Toaddress these challenges, we introduce SegDiff, a closed-loop visuomo-tor policy that integrates the strengths of both paradigms. SegDiff de-composes demonstrations into motion segments between keyposes andlearns to predict the continuous trajectory from the current state tothe next keypose, enabling long-horizon prediction with real-time refine-ment. Furthermore, we leverage the capability of diffusion models andDDIM inversion to propose a Dynamic Temporal Ensembling mecha-nism, which allows the policy to efficiently respond to dynamic environ-ments and mitigate discontinuities caused by inconsistent multi-modalsampling. SegDiff demonstrates significant performance gains over exist-ing approaches across various simulated and real-world scenarios, indi-cating its strong ability to reason over extended temporal dependencieswhile maintaining real-time adaptability and control stability.

ECCV 2026

Scaling Laws for Black-box Adversarial Attacks

Chuan Liu, Huanran Chen, Yichi Zhang, Jun Zhu, Yinpeng Dong

Adversarial examples exhibit cross-model transferability, en-abling threatening black-box attacks on commercial models. Model en-sembling, which attacks multiple surrogates, is a known strategy to im-prove this transferability. However, prior studies typically use small, fixedensembles, which leaves open an intriguing question of whether scalingthe number of surrogate models can further improve black-box attacks.In this work, we conduct the first large-scale empirical study of thisquestion. We show that by resolving gradient conflict with advanced op-timizers, we overcome the quantitative limitations of idealized theoreticalbounds to empirically discover a robust log-linear scaling law, demon-strating that the Attack Success Rate scales linearly with the logarithmof the ensemble size T . We rigorously verify this law across standardclassifiers, SOTA defenses, and MLLMs, and find that scaling distills ro-bust, semantic features of the target class. Consequently, we apply thisfundamental insight to benchmark SOTA MLLMs. This reveals both theattack’s devastating power and a clear robustness hierarchy, as evidencedby achieving an over 80% transfer attack success rate on proprietarymodels like GPT-4o, while also highlighting the exceptional resilienceof Claude-3.5-Sonnet. Our findings urge a shift in focus for robustnessevaluation: from designing intricate algorithms on small ensembles tounderstanding the principled and powerful threat of scaling.

ECCV 2026

RoMa v2: Harder Better Faster Denser Feature Matching

Johan Edstedt, David Nordström, Yushan Zhang, Georg Bökman*, Jonathan Astermark, Viktor Larsson, Anders Heyden, Fredrik Kahl, Mårten Wadenbäck, Michael Felsberg

Dense feature matching aims to estimate all correspondencesbetween two images of a 3D scene and has recently been established asthe gold standard due to its high accuracy and robustness. However,existing dense matchers still fail or perform poorly for many hard real-world scenarios, and high-precision models are often slow, limiting theirapplicability. In this paper, we attack these weaknesses on a wide frontthrough a series of systematic improvements that together yield a sig-nificantly better model. In particular, we construct a novel matchingarchitecture and loss, which, combined with a curated diverse trainingdistribution, enables our model to solve many complex matching tasks.We further make training faster through a decoupled two-stage matching-then-refinement pipeline, and at the same time, significantly reduce re-finement memory usage through a custom CUDA kernel. Finally, weleverage the recent DINOv3 foundation model along with multiple otherinsights to make the model more robust and unbiased. In our extensiveset of experiments, we show that the resulting novel matcher sets a newstate-of-the-art, being significantly more accurate than its predecessors.ω

ECCV 2026

Reliable Reasoning in SVG-LLMs via Multi-Task Multi-Reward Reinforcement Learning

Haomin Wang, Qi Wei, Qianli Ma, Shengyuan Ding, Jinhui Yin, Kai Chen, Hongjie Zhang

With the rapid advancement of vision–language models, anincreasing number of studies have explored their potential for SVG gener-ation tasks. Although existing approaches improve performance by con-structing large-scale SVG datasets and introducing SVG-specific tokens,they still suffer from limited generalization, redundant paths in code out-puts, and a lack of explicit reasoning. In this work, we present CTRL-S(Chain-of-Thought Reinforcement Learning for SVG), a unified frame-work that introduces a chain-of-thought mechanism to explicitly exposethe model’s reasoning process during SVG generation. To support thisstructured reasoning, we construct SVG-Sophia, a high-quality datasetcontaining 145K samples across SVG code refinement, Text-to-SVG, andImage-to-SVG tasks. By training the model to generate group-level struc-tured SVG code, CTRL-S significantly improves structural coherenceand visual fidelity. Furthermore, we adopt the GRPO algorithm and de-sign a multi-reward optimization framework, incorporating DINO, im-age–text similarity, format, and code efficiency rewards. Through jointmulti-reward optimization and multi-task training, our approach system-atically enhances overall generation capabilities. Extensive experimentsshow that CTRL-S outperforms existing methods, achieving higher tasksuccess rates, superior SVG code quality, and exceptional visual fidelity.Our code is available at https://github.com/hmwang2002/CTRL-S.

ECCV 2026

RaPTGS: Render-Agnostic Post-Training Compression of 3D Gaussian Splatting

Asif Jawad, K. M. Azwad Hossain

3D Gaussian Splatting (3DGS) has emerged as a powerfultechnique for real-time, high-fidelity novel view synthesis. Despite theseadvantages, its multi-million point representation leads to large stor-age footprints that hinder asset distribution and on-device deployment.Existing compression approaches typically rely on access to training im-ages, camera poses, or the rendering pipeline for retraining or iterativefine-tuning. However, in realistic post-training and archival scenarios,often only the optimized model parameters are available. To addressthis limitation, we propose a render-agnostic, post-training compressionpipeline for 3DGS operating strictly under a model-only constraint. Wefirst introduce a camera-independent, multi-criteria importance score toprune redundant Gaussians based on a combination of geometric, spa-tial, and appearance-related cues. Following this pruning, a lightweight,training-free refinement step conservatively restores local coverage. Tofurther reduce the storage footprint, we apply degree-wise vector quanti-zation to the spherical harmonic coefficients and compress the remainingattributes via entropy coding. The compression performance is signifi-cantly enhanced by a custom spatial reordering to maximize local dataredundancy. Experiments on standard benchmarks show that our ap-proach achieves an average of 30× compression rate while maintainingcompetitive visual quality against existing methods.

ECCV 2026

R3RECON: Radiance-Field-Free Active Reconstruction via Renderability

Xiaofeng Jin, Matteo Frosi, Yiran Guo, Matteo Matteucci

In active reconstruction, an embodied agent must decidewhere to look next to efficiently acquire views that support high-qualitynovel-view rendering. Recent work on active view planning for neuralrendering largely derives next-best-view (NBV) criteria by backpropa-gating through radiance fields or estimating information entropy over3D Gaussian primitives. While effective, these strategies tightly cou-ple view selection to heavy, representation-specific mechanisms and failto account for the computational and resource constraints required forlightweight online deployment. In this paper, we revisit active recon-struction from a renderability-centric perspective. We propose R3CON,a radiance-fields-free active reconstruction framework that induces an im-plicit, pose-conditioned renderability field over SE(3) from a lightweightvoxel map. Our formulation aggregates per-voxel online observation statis-tics into a unified scalar renderability score that is cheap to update andcan be queried in closed form at arbitrary candidate viewpoints in mil-liseconds, without requiring gradients or radiance-field training. This ren-derability field is strongly correlated with image-space reconstruction er-ror, naturally guiding NBV selection. We further introduce a panoramicextension that estimates omnidirectional (360◦ ) view utility to acceleratecandidate evaluation. In the standard indoor Replica dataset, R3CONachieves more uniform novel-view quality and higher 3D Gaussian splat-ting (3DGS) reconstruction accuracy than recent active GS baselineswith matched view and time budgets. .

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