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ECCV 2026

Towards Robustness against Typographic Attack with Training-free Concept Localization

Bohan Liu, Wenqian Ye, Guangzhi Xiong, Zhenghao He, Sanchit Sinha, Aidong Zhang

Models trained via Contrastive Language-Image Pretrain-ing (CLIP) serve as the foundational vision encoders for many modernLarge Vision Language Models (LVLMs). Despite their widespread adop-tion, CLIP models exhibit a critical yet underexplored failure mode:irrelevant text appearing within images confounds visual representa-tions, biasing them toward lexical meaning rather than true visual se-mantics. This robustness issue, commonly described as a TypographicAttack (TA), exposes a vulnerability that poses a significant risk tosafety-critical applications such as autonomous driving. To achieve in-terpretable and effective robustness against TA, we propose a novel,training-free mechanistic interpretability method. Our method providessampling-based interpretations of hidden state representations and quan-titatively attributes semantic versus lexical focus to individual attentionheads. Through probabilistic analysis and circuit mining, we isolate spe-cific Vision Transformer (ViT) components that disproportionately en-code lexical information, thereby identifying the mechanistic source ofTA. We further show that simple interventions applied directly to theidentified circuits, without any additional training, can substantially im-prove robustness against Typographic Attacks in object classification.These interventions, such as selective adjustment of attention weights,outperform both supervised and training-free defense methods. Our ex-periments also demonstrate that applying the proposed intervention tothe vision encoders of several state-of-the-art LVLMs yields substantialgains in Visual Question Answering accuracy under Typographic At-tack interference on RIO-Bench. These results confirm both the efficacyand the generalizability of our mechanistic approach. Code is released athttps://github.com/Liu-524/SamplingTAR.

ECCV 2026

Text-based Tactile Graphics Generation for the Visually Impaired

Ruihan Gao, Joonghyuk Shin, Ava Pun, Jaesik Park, Wenzhen Yuan, Jun-Yan Zhu

Tactile graphics are a primary medium for blind and low-vision (BLV) individuals to access non-textual information. However,they are difficult to scale or personalize. While recent generative modelshave revolutionized visual content creation, they are optimized for screen-based visual realism and fail to satisfy the haptic perceptual and physicalfabrication constraints required for touch. We present the first integratedgenerative system that produces fabrication-ready 2.5D tactile graphicsdirectly from natural language prompts, jointly generating global basegeometry, fine-grained tactile surface textures, and standard-compliantbraille within a unified 3D-printable representation. Our approach intro-duces fabrication-aware techniques, including template-guided relief gen-eration, a fast diffusion-based text-to-texture module for high-resolutiontileable normal maps, and strict base flattening to ensure tactile readabil-ity and printability, while supporting both automatic generation and in-teractive texture control. Extensive evaluations, together with in-personuser studies with BLV participants and blindfolded sighted participantsusing physically 3D-printed outputs, show that participants consistentlyprefer our results over baselines. By extending generative graphics be-yond screens to touchable reliefs, our work broadens access to generativeAI for the BLV community and beyond. The code and project webpageare available at https://ruihangao.github.io/Text2TactileGraphics/.

ECCV 2026

Test Time Training for Long Videos via Frame Forgetting Network

Rajat Modi, Xin LIANG, Sebastian Noel, Yogesh Rawat

Test Time Training (TTT) is a mechanism in which a modeladapts to an incoming test-sample by performing some self-supervised(SSL) task and updating its weights even during inference. This proce-dure does not require labels at test-time. This paper focuses on TTT forlong-videos. A major concern with existing approaches is: 1) they per-form TTT updates using a sliding window containing frames in the past,whose compute increases linearly with the size of window. This becomescomputationally intractable when the videos are hours long. 2) TTT isperformed even when temporally close frames look similar, thereby con-suming a lot of compute.We present the Frame Forgetting Network (FFN) that: 1) operates ononly three frames within the sliding window, namely the frame that ex-its, the current frame and the frame after that. The model still managesto retain temporal context and work for hours long-videos; 2) mathemat-ically define a ‘surprise’ metric: how much ‘new information’ the incom-ing frame contains with respect to the past seen frame. This facilitatesdetermining how to modify the effective window size during TTT andconstitutes the core mechanism of an adaptive windowing algorithm. Ad-ditionally, we curate a dataset EpicTours containing up to 3 hour longvideos of walking city-tours, whereas earlier datasets on this problemwere only 5 min long. We demonstrate FFN’s empirical effectiveness ondense-segmentation, video classification tasks, generalization to depth-estimation, and multi-hour long videos. The project page can be foundat https://github.com/rajatmodi62/ffn.

ECCV 2026

SyncCache: Exploiting Asymmetric Dynamics for Fast Audio-Driven Portrait Animation

Juncheng Ma, Yuxuan Du, Sun Yanan, Zhening Xing, Changlin Li, Zhenyu Tang, Bo Li, Peng-Tao Jiang, Li Yuan, Daquan Zhou, Yonghong Tian

Diffusion Transformers (DiTs) have significantly advancedaudio-driven portrait animation, but their high computational cost leadsto substantial inference latency. Although training-free diffusion cachingaccelerates inference significant, existing methods are primarily devel-oped for text-conditioned generation and overlook the spatial and modal-ity imbalances inherent in audio-driven portrait animation. In this pa-per, we propose SyncCache, a training-free caching acceleration methodtailored for DiT-based portrait animation that explicitly exploits asym-metric dynamics. Specifically, high-frequency dynamics driven by audioconditions and concentrated in human regions are more challenging andcritical to cache and reuse than the low-frequency visual background inportrait animation. First, we introduce Spatially-Asymmetric Probingto prioritize error sensitivity in dynamic human region. Second, throughModality-Decoupled Caching, we bypass heavy DiT block by reusingstable inter-block residuals, while continuously recomputing lightweightaudio blocks to preserve precise lip synchronization. Furthermore, weintroduce a cache ratio to control cache capacity and formulate memory-adaptive cache selection as an offline dynamic programming problemwithout online overhead. Extensive experiments demonstrate that Sync-Cache achieves superior speed–quality trade-offs, delivering up to 4.12×acceleration on HunyuanVideo-Avatar and 3.75× on Wan-S2V with near-lossless visual fidelity and precise audio alignment.

ECCV 2026

StreetForward: Perceiving Dynamic Street with Feedforward Causal Dynamics

Zhongrui Yu, Zhao Wang, Yida Wang, Xueyang Zhang, Yifei Zhan, Kun Zhan

Feedforward reconstruction is crucial for autonomous drivingapplications, where rapid scene reconstruction enables efficient utiliza-tion of large-scale driving datasets in closed-loop simulation and otherdownstream tasks, eliminating the need for time-consuming per-scene op-timization. We present StreetForward, a pose-free and tracker-free feed-forward framework for dynamic street reconstruction. Building uponthe alternating attention mechanism from Visual Geometry GroundedTransformer (VGGT), we propose a simple yet effective temporal maskattention module that captures dynamic motion information from im-age sequences and produces motion-aware latent representations. Staticcontent and dynamic instances are represented uniformly with 3D Gaus-sian Splatting, and are optimized jointly by cross-frame rendering withspatio-temporal consistency, allowing the model to infer per-pixel ve-locities and produce high-fidelity novel views at new poses and times.We train and evaluate our model on the Waymo Open Dataset, demon-strating superior performance on novel view synthesis and depth estima-tion compared to existing methods. Furthermore, zero-shot inference onCARLA validates the generalization capability of our approach.

ECCV 2026

SONIC: Spectral Optimization of Noise for Inpainting with Consistency

Seungyeon Baek, Erqun Dong, Shadan Namazifard, Mark J Matthews, Kwang Moo Yi

We propose a novel training-free method for inpainting with off-the-shelf text-to-image models. While guidance-based methods in theory allow generic models to be used for inverse problems such as inpainting — in practice their effectiveness is limited, leading to the necessity of specialized inpainting-specific models. In this work, we argue the missing ingredient for training-free generic model usage is proper optimization of the initial noise sample. We optimize the initial noise to approximately reproduce the unmasked image, in as few as tens of optimization steps, then use it with a conventional training-free inpainting method. Critically, we propose two core ideas that make this possible: (i) we perform linear approximation that avoids the costly and often impractical unrolling required to relate the initial noise sample to model output—which potentially is why this relationship was previously overlooked; and (ii) perform spectral preconditioning by optimizing the initial noise sample in the spectral domain with Adam, which stabilizes the optimization. We demonstrate our method on various inpainting tasks, outperforming the state of the art. Project website: https://ubc-vision.github.io/sonic/

ECCV 2026

SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation

Víctor Barreiro, Johannes Jakubik, Francisco Argüello, Dora Heras

Fine-tuning foundation models for Earth Observation is com-putationally expensive, with high training time and memory demands forboth training and deployment. Parameter-efficient methods reduce train-ing cost but retain full inference complexity, while post-hoc compressionoptimizes inference only after costly full fine-tuning. We introduce SIM-PLER, a pre–fine-tuning architecture selection method that reduces in-ference and deployment costs by identifying an effective model depthbefore adaptation. SIMPLER exploits stabilization of representations indeeper layers of pre-trained vision transformers: it computes layer-wiserepresentation similarity on unlabeled task data and applies an auto-mated scoring function to select redundant layers, with no gradients,magnitude heuristics, or hyperparameter tuning required. On Prithvi-EO-2, SIMPLER prunes up to 79% of parameters while retaining 94% ofbaseline performance, yielding a 2.1× training speedup and 2.6× infer-ence speedup. The method generalizes to TerraMind (a multimodal EOfoundation model) and ImageNet-pretrained ViT-MAE, demonstratingapplicability across tasks, architectures, and spectral modalities. Code isavailable at https://gitlab.citius.gal/hpc4rs/simpler.

ECCV 2026

SFKD: Spatial–Frequency Joint-Aware Heterogeneous Knowledge Distillation via Multi-Level Wavelet Spectral Interaction

Cuipeng Wang, Haipeng Wang

Most existing knowledge distillation methods focus on ho-mogeneous models (e.g., CNN→CNN), thereby overlooking the flexi-bility and potential of knowledge transfer across heterogeneous mod-els. Due to intrinsic inductive bias discrepancies between heterogeneousmodels that cause spatial distribution inconsistencies, prior heteroge-neous distillation methods often weaken or discard spatial informationin heterogeneous representations. However, the spatial information inrepresentations often encodes transferable global structural semantics aswell as architecture-specific local details, and therefore should not bedirectly ignored. To better leverage the spatial information encoded inheterogeneous representations, we propose a Spatial–Frequency Joint-Aware Heterogeneous Knowledge Distillation framework (SFKD). Byleveraging the complementary properties of wavelet transform spatiallocality and Fourier representations in characterizing global energy dis-tributions, we first apply multi-level discrete wavelet transform to ex-plicitly decouple spatial information. The resulting wavelet sub-bandsare further refined by a dual-stream dual-stage refinement module, andfinally combined with a Gaussian-filtered frequency loss to selectivelycapture informative global information. Extensive experiments on mul-tiple benchmark datasets under both homogeneous and heterogeneousmodels demonstrate the superiority of our method. Code is available athttps://github.com/cpcpWang/SFKD.

ECCV 2026

SDSA: Shallow-Deep Squeezing Adapter for Vision-Language Models

Hao Wang, Rui Zhu, Xiaoxu Li, Zhanyu Ma, Jing-Hao Xue

Recent adapters for vision-language models (VLMs) still suf-fer from dense cross-modal interactions and limited control over align-ment capacity. To address this issue, we propose a two-stage Shallow-Deep Squeezing Adapter (SDSA) that explicitly regulates cross-modalinteraction density and alignment capacity. In the shallow squeezingstage, SDSA leverages token-level masking to impose structured spar-sity within a sparse bottleneck space. Then, in the deep squeezing stage,SDSA applies a shared low-rank transformation and a cross-modal atten-tion module: the low-rank module consolidates alignment into a compactset of dominant shared directions, while cross-modal attention refinesrepresentations through selective interaction. Extensive experiments on11 datasets show that SDSA delivers superior base-to-novel generaliza-tion and cross-dataset evaluation, and significantly improves the gener-alizability of VLMs under few-shot conditions. The code can be found athttps://github.com/haowang-ac/SDSA.

ECCV 2026

Scale3D: Autoregressive Modeling for Large Outdoor Scene Generation

DI QI, Zheng Sun, Xuanyang Zhang, Gang Yu

We present Scale3D, an autoregressive model for generatinghigh-quality outdoor 3D scenes at scale. Scaling 3D generation to vastenvironments like city blocks usually degrades local geometric detailsas the layout expands. Scale3D decouples this problem into a two-stageprocess. A causal autoregressive model _x001C_rst plans the global layout bypredicting scene chunks over a 2D spatial grid. A masked autoregressivemodel then synthesizes the actual 3D geometry within each chunk. Wereconstruct these local structures using a 3D chunk-based VAE designedto preserve sharp edges and high-_x001C_delity surfaces. We also introduce apipeline that extracts spatial relationships directly from geometry, al-lowing users to control complex scene layouts via simple text prompts.Unlike patch-based di_x001B_usion models that struggle with boundary arti-facts, Scale3D maintains long-range spatial coherence across expansiveoutdoor datasets while rendering detailed 3D structures.

ECCV 2026

RT-RMOT: A Dataset and Framework for RGB-Thermal Referring Multi-Object Tracking

Yanqiu Yu, Zhifan Jin, Sijia Chen, Tongfei Chu, En Yu, Liman Liu, Wenbing Tao

Referring Multi-Object Tracking has attracted increasing at-tention due to its human-friendly interactive characteristics, yet it ex-hibits limitations in low-visibility conditions, such as nighttime, smoke,and other challenging scenarios. To overcome this limitation, we proposea new RGB-Thermal RMOT task, named RT-RMOT, which aimsto fuse RGB appearance features with the illumination robustness ofthe thermal modality to enable all-day referring multi-object tracking.To promote research on RT-RMOT, we construct the first Ref erringMulti-Object Tracking dataset under RGB-Thermal modality, namedRefRT. It contains 388 language descriptions, 1,250 tracked targets,and 166,147 Language-RGB-Thermal (L-RGB-T) triplets. Furthermore,we propose RTrack, a framework built upon a multimodal large lan-guage model (MLLM) that integrates RGB, thermal, and textual fea-tures. Since the initial framework still leaves room for improvement, weintroduce a Group Sequence Policy Optimization (GSPO) strategy tofurther exploit the model’s potential. To alleviate training instabilityduring RL fine-tuning, we introduce a Clipped Advantage Scaling (CAS)strategy to suppress gradient explosion. In addition, we design StructuredOutput Reward and Comprehensive Detection Reward to balance explo-ration and exploitation, thereby improving the completeness and accu-racy of target perception. Extensive experiments on the RefRT datasetdemonstrate the effectiveness of the proposed RTrack framework.

ECCV 2026

RotateAttention : RoPE-Aware Rotation and Range Rectification for INT4 Quantized Attention in Video Generation

Yaofu Liu, Wangli Lan, Jinxi Li, Binhang Yuan, Harry Yang

In DiT-based video generation models equipped with3D Rotary Position Embeddings (3D RoPE), the attention mech-anism remains a primary computational bottleneck due to its quadraticcomplexity with respect to sequence length. While quantized FlashAt-tention o_x001B_ers a promising path toward hardware acceleration, exist-ing low-bit quantization methods overlook two critical challenges in thissetting: 1) applying online rotation matrices_x0016_a widely used techniquefor mitigating outliers in Queries (Q) and Keys (K )_x0016_is di_x001E_cult toreconcile with RoPE; and 2) the non-negative attention matrix P =exp(QK − max(QK)) makes symmetric quantization waste half of the 4-bit dynamic range. In this work, we observe that the outlier distributionsof Q and K are strongly a_x001B_ected by the dimensional partitioning of 3DRoPE. Based on this _x001C_nding, we propose RotateAttention, an e_x001E_-cient mixed-precision INT4 FlashAttention framework tailored forDiT-based video generation models with 3D RoPE, using selec-tive FP16 fallback for accuracy-sensitive attention blocks and denoisingsteps. RotateAttention introduces two core techniques: 1) RoPE-awareRotation, which employs either mergeable rotation matrices that canbe fused into RoPE or negligible-overhead matrices to mitigate RoPE-induced outliers in Q and K ; and 2) Range-optimized P Quantiza-tion, which uses _x001C_xed scales and zero-points to fully exploit the INT4numerical range with minimal computational overhead. Experimentsshow that RotateAttention preserves video generation quality nearlyidentical to full-precision baselines while achieving up to 1.68× end-to-end speedup and 2.2× kernel-level acceleration.

ECCV 2026

Repurposing Geometric Foundation Models for Multi-view Diffusion

Wooseok Jang, Seonghu Jeon, Jisang Han, Jinhyeok Choi, Minkyung Kwon, Seungryong Kim, Saining Xie, Sainan Liu

The latent space of diffusion models fundamentally deter-mines their learning efficiency and generation quality. While recent ad-vances in the latent space have driven substantial progress in single-imagegeneration, the optimal latent space for novel view synthesis (NVS) re-mains largely unexplored. In particular, NVS requires geometrically con-sistent generation across viewpoints, but existing approaches typicallyoperate in a view-independent latent space. In this paper, we proposeGeometric Latent Diffusion (GLD), a framework that repurposesthe feature space of a geometric foundation model as the latent space formulti-view diffusion. We show that the features of the geometric founda-tion model not only support high-fidelity RGB reconstruction but alsoencode strong cross-view geometric correspondences, providing a well-suited latent space for NVS. Through experiments, GLD outperformsboth VAE and RAE on 2D image quality and 3D consistency metrics,accelerating training by more than 4.4× compared to the VAE latentspace. Notably, GLD remains competitive with state-of-the-art methodsthat leverage large-scale text-to-image pretraining, despite training itsdiffusion model from scratch without such generative pretraining.

ECCV 2026

Reinforcing Video Reasoning with Focused Thinking

Jisheng Dang, Jingze Wu, Teng Wang, Xuanhui Lin, Nannan Zhu, Hongbo Chen, Weishi Zheng, Meng Wang, Tat-Seng Chua

Recent advancements in reinforcement learning, particularlythrough Group Relative Policy Optimization (GRPO), have significantlyimproved multimodal large language models for complex reasoning tasks.However, two critical limitations persist: 1) they often produce unfo-cused, verbose reasoning chains that obscure salient spatiotemporal cues,and 2) binary rewarding fails to account for partially correct answers,resulting in high reward variance and ine!cient learning. In this pa-per, we propose TW-GRPO, a novel framework that enhances visualreasoning with focused thinking and dense reward granularity. Specifi-cally, we employ a token weighting mechanism that prioritizes tokenswith high informational density (estimated by intra-group informationentropy), suppressing redundant tokens like generic reasoning prefixes.Furthermore, we reformulate RL training by shifting from single-choice tomulti-choice QA tasks, where soft rewards enable finer-grained gradientestimation by distinguishing partial correctness. Additionally, we proposequestion-answer inversion, a data augmentation strategy to generatediverse multi-choice samples from existing benchmarks. Experimentsdemonstrate superior performance on several video reasoning and un-derstanding benchmarks. Notably, under identical training settings, ourmethod significantly outperforms the Video-R1, achieving an averageimprovement of 2.6% across four comparable benchmarks, e.g., +3.8%on MVBench, +2.4% on TempCompass, and +2.3% on VideoMME. Ourcodes are available at https://github.com/longmalongma/TW-GRPO.

ECCV 2026

Reconstruction-Anchored Diffusion Model for Text-to-Motion Generation

Yifei Liu, Changxing Ding, Ling Guo, Huaiguang Jiang, Qiong Cao

Diffusion models have seen widespread adoption for text-driven human motion generation and related tasks due to their impres-sive generative capabilities and flexibility. However, current motion dif-fusion models face two major limitations: a representational gap causedby pre-trained text encoders that lack motion-specific information, anderror propagation during the iterative denoising process. This paper in-troduces Reconstruction-Anchored Diffusion Model (RAM) to addressthese challenges. First, RAM leverages a motion latent space as interme-diate supervision for text-to-motion generation. To this end, RAM co-trains a motion reconstruction branch with two key objective functions:self-regularization to enhance the discrimination of the motion space andmotion-centric latent alignment to enable accurate mapping from textto the motion latent space. Second, we propose Reconstructive ErrorGuidance (REG), a testing-stage guidance mechanism that exploits themotion diffusion model’s inherent self-correction ability to mitigate errorpropagation. At each denoising step, REG uses the motion reconstruc-tion branch to reconstruct the previous estimate, reproducing the priorerror patterns. By amplifying the residual between the current predic-tion and the reconstructed estimate, REG highlights the improvementsin the current prediction. Extensive experiments demonstrate that RAMachieves significant improvements and state-of-the-art performance. Ourcode will be released on https://feifeifeiliu.github.io/RAM .

ECCV 2026

Real-Time LiDAR Gaussian Splatting SLAM via Geometry-Aware Covariance Coupling

SeungJun Tak, Yewon Jeon, Hwang Jaeik, Suk Min Hwang, SeongboHa SeongboHa, Hyeonwoo Yu*

We present a real-time LiDAR-based framework for Gaus-sian Splatting SLAM that tightly couples fast G-ICP registration withspherical rasterization-based dense mapping for large-scale sequences.Leveraging LiDAR geometry rather than appearance, we reuse tracking-estimated local covariances to initialize Gaussians with range-aware scalesand to derive surface normals for geometry-aware map optimization. Wefurther introduce a covariance-derived geometry score that measures lo-cal complexity and drives pruning in planar regions and selective densifi-cation in structurally rich areas, while optimized Gaussians and LiDAR-specific confidence cues are fed back to improve tracking robustness. Onthe Newer College dataset, our method achieves an F-score of 86.78%using purely online trajectories at real-time speed (>20 FPS), and addi-tional experiments on other datasets confirm its stability and scalability.Code is available at github.com/Lab-of-AI-and-Robotics/LiDAR-GS-SLAM.Project Page: lab-of-ai-and-robotics.github.io/GS-SLAM-Family

ECCV 2026

Proposal Score Realignment Guided by Semantic Completeness for Weakly Supervised Temporal Action Localization

Maodong Li, Zhihao Wang, Jingxiong Wang, Jian Wang, Bing Li

Due to the absence of temporal annotations, recent weaklysupervised temporal action localization (WTAL) methods often adoptpseudo-label learning to boost localization. A segment-level WTAL base-line first generates proposals and constructs pseudo labels, which thensupervise a fully supervised localization head. However, the constructionof pseudo labels depends on the proposals’ original confidence scores.Accumulated noise in proposal generation induces a mismatch betweenthese confidence scores and proposal quality, thereby limiting the achiev-able quality of the pseudo labels. To address this, we propose SCLR, aproposal-level score realignment framework driven by Semantic Com-pleteness Learning (SCL). SCL proceeds in three steps. First, for eachaction class, we construct a core semantic center that focuses on localhigh-response cues and a global teacher center that aggregates class-levelcontextual commonalities. Second, guided by the two centers, we self-supervise the decomposition of the core semantic center into generic andspecific components, then reconstruct the class center by a weighted sum.Third, we derive soft labels from proposal–to–center similarity to super-vise a per-proposal semantic-completeness score, which is then used forscore realignment. Extensive experiments on two public datasets demon-strate that SCLR, when integrated as a plug-and-play framework, consis-tently reduces score misalignment across diverse baselines and improveslocalization accuracy. Further, leveraging re-aligned proposals as pseudolabels to train the localization head yields state-of-the-art performance.

ECCV 2026

PLOT: Pseudo-Labeling via Object Tracking for Monocular 3D Object Detection

SeokYeong Lee, Sithu Aung, JunYong Choi, Seungryong Kim, Ig-Jae Kim, Junghyun Cho

Monocular 3D object detection is crucial for scalable per-ception across fields like autonomous driving, robotics, and surveillance.However, progress is hindered by limited 3D annotations and the in-herent ambiguity of single-image geometry. Existing methods often relyon strong geometric assumptions or carefully curated datasets, whichlimit their applicability to real-world scenarios. In this paper, we presentPLOT (Pseudo-Labeling via Object Tracking), a framework that gen-erates 3D annotations from monocular videos without auxiliary sen-sors or model retraining. PLOT tracks object and background trajecto-ries to estimate camera motion and perform object association in pose-unknown settings. These trajectories provide point correspondences thatalign frame-wise pseudo-LiDARs, which are then fused via simple opti-mization into a unified object shape robust to occlusion and viewpointshifts. Recognizing temporal coherence as a fundamental requirementfor reliable shape fusion and video perception, we design a global ob-ject memory that preserves consistent object identities across frames.PLOT achieves robust annotation quality and strong generalization onboth M3OD video benchmarks and in-the-wild videos, proving its effec-tiveness across diverse and unconstrained domains. Project page: https://plot-eccv.github.io.

ECCV 2026

Personalizing MLLMs via Reinforced Multimodal Reference Game

Deepayan Das, Davide Talon, Yiming Wang, Massimiliano Mancini, Elisa Ricci

Personalizing Multimodal Large Language Models (MLLMs)aims to recognize users’ unique concepts from visual data and providepersonalized responses. Although prior work has shown the benefit ofconcept descriptions and reasoning for this task, MLLM descriptions ofteninclude information, such as state and context, that does not help andmay in fact hinder the unique identification of the target concept amongother visually similar items. Effective descriptions of personal conceptsshould instead be accurate, discriminative, and free of distracting details.To achieve such descriptions, we introduce Reinforced Reference Game(RRG), a learning framework that promotes discriminative descriptionsthrough a novel reinforced multimodal reference game. The MLLM playsboth the roles of speaker and listener in a contrastive game setting, whosegoal is to effectively communicate discriminative information about atarget concept. Our approach formulates a verifiable contrastive rewardover hard positives (dissimilar views of the same concept) and hardnegatives (visually similar but different concepts). Empirically, RRGachieves state-of-the-art across multiple tasks on three personalizationbenchmarks. RRG generalizes to unseen domains and outperforms existingmethods based on concept descriptions and personalization-specific RLframeworks. We will release code and models in the project page.

ECCV 2026

Off the Planckian Locus: Using 2D Chromaticity to Improve In-Camera Color

SaiKiran Tedla, Joshua Little, Hakki Karaimer, Michael S. Brown

Traditional in-camera colorimetric mapping relies on corre-lated color temperature (CCT)–based interpolation between pre-calibratedtransforms optimized for Planckian illuminants such as CIE A and D65.However, modern lighting technologies such as LEDs can deviate sub-stantially from the Planckian locus, exposing the limitations of rely-ing on conventional one-dimensional CCT for illumination characteriza-tion. This paper demonstrates that transitioning from 1D CCT (on thePlanckian locus) to a 2D chromaticity space (off the Planckian locus) im-proves colorimetric accuracy across various mapping approaches. In addi-tion, we replace conventional CCT interpolation with a lightweight multi-layer perceptron (MLP) that leverages 2D chromaticity features for ro-bust colorimetric mapping under non-Planckian illuminants. A lightbox-based calibration procedure incorporating representative LED sourcesis used to train our MLP. Validated across diverse LED lighting, ourmethod reduces angular reproduction error by 22% on average in LED-lit scenes, maintains backward compatibility with traditional illuminants,accommodates multi-illuminant scenes, and supports real-time in-cameradeployment with negligible additional computational cost. Code and datacan be found on the project webpage: ccmmlp.github.io

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