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ECCV 2026

Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification

Stanisław Janik, Michal Byra

Implicit Neural Representations (INRs) encode signals as the weights of a coordinate-based neural network and have recently been proposed as an alternative domain for downstream learning. While promising, classification directly in weight space remains challenging due to the high dimensionality and complex structure of INR parameters. Furthermore, the way discriminative information is distributed across INR weights remains poorly understood. We propose a hierarchical Mixture-of-Experts (HMoE) Transformer that processes INR weights using conditional computation aligned with the structure of the underlying implicit network. Coupled with a meta-learning framework that shapes INR parameters for downstream tasks, our model achieves state-of-the-art accuracy across standard benchmarks, ranging from low-resolution datasets to high-resolution ImageNet-1K. To gain insight into how INRs encode discriminative information, we develop weight-space attribution and pruning methods that identify parameters most relevant for classification. These analyses reveal how class-specific structure emerges within INR layers and support the suitability of MoE architectures for weight-space learning. Our approach advances both the performance and interpretability of weight-space classifiers.

ECCV 2026

RCEdit-500K: Reference Completion for Image-Conditioned Image Editing

Jingxu Zhang, Daneul Kim, Yueming Pan, Dong Chen, Kai Qiu, Yang Liu, Yifan Yang, Qi Dai, Xiaoyan Sun, Chong Luo

Image-conditioned image editing (ICIE) guides edits with a reference image to convey visual attributes—such as style, tone, or object identity—that are difficult to specify through text alone. Despite growing practical demand, no large-scale unified ICIE dataset currently exists in the open-source ecosystem; existing efforts cover only a narrow subset of edit types at small scale, typically constructed via costly forward synthesis. We reformulate ICIE data construction as a reference-completion problem: high-quality text-conditioned image editing (TCIE) datasets already supply the input image, instruction, and edited target, and can be augmented with aligned reference images through type-specific synthesis and lightweight instruction adaptation. Building on this insight, we propose a scalable pipeline equipped with weak-instruction augmentation and five-dimensional VLM-based post-filtering, and use it to construct RCEdit-500K—the first large-scale unified open ICIE dataset comprising 477K quadruplets across six edit categories (add, remove, replace, background, style, alter) with both concrete and abstract reference types. Training on RCEdit-500K consistently improves reference-guided editing: LoRA adaptation on diffusion models yields up to +1.22 average gain, and an autoregressive model without native editing ability acquires competitive ICIE performance, demonstrating that data availability is the primary bottleneck for open-source ICIE.

ECCV 2026

Video Generation Models are General-Purpose Vision Learners

Letian Wang, Chuhan Zhang, Rishabh Kabra, Jasper Uijlings, Steven Waslander, Andrew Zisserman, Joao Carreira, Kaiming He, Mykhaylo Andriluka, Eduard Gabriel Bazavan, Andrei Zanfir, Cristian Sminchisescu

Driven by next-token prediction, NLP shifted from taskspecific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision? In this paper, we contend that large-scale text-tovideo generation serves as a strong pre-training paradigm for computer vision, providing the necessary spatiotemporal priors, vision-language alignment, and scalability required for general visual intelligence. We introduce GenCeption, which leverages a pre-trained video generative diffusion backbone to define a feed-forward perception model, capable of performing various vision tasks steered by text instructions. Empirical results demonstrate that GenCeption achieves state-of-the-art performance across a diverse suite of tasks, including depth, surface normal, and camera pose estimation, expression-referring segmentation, and 3D keypoint prediction, often matching or surpassing specialized models (e.g., DepthAnything V3, SegmentAnything V3). Furthermore, the video generative pretrained backbone outperforms alternative pretraining paradigms (e.g., V-JEPA, and Video MAE) under comparable settings. Importantly, GenCeption exhibits preliminary data and model scaling properties along with exceptional data efficiency where it achieves comparable performance with leading models like D4RT and VGGT-Ω with 7× to 500× less training data. Finally, GenCeption also exhibits intriguing emergent behaviors: a model trained exclusively on synthetic human videos generalizes to real-world footage and out-of-distribution object categories (e.g., animals and robots). These findings suggest that video generation is not merely a synthesis tool, but a foundational path toward generalist vision intelligence for the physical world.

ECCV 2026

AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer

Yongwen Lai, Chaoqun Wang

Image-guided style transfer aims to apply the artistic characteristics of a style image to a content image while preserving its semantic structure and layout. Despite advances in diffusion-based methods, existing approaches often face challenges in disentangling content and style, particularly when independently optimized adapters are naively combined, causing conflicts between adapters and limiting controllability over the content-style balance in inference. We further demonstrate that training-free structural guidance directly derived from the content image through the internal attention of pre-trained model outperforms a dedicated content LoRA adapter in terms of structural fidelity and computational efficiency. Building on these observations, we propose AnyStyle, a streamlined framework for image-guided style transfer. The framework adopts a unified single-adapter paradigm for coherent style capture from the style image and incorporates training-free structural guidance from the content image, thus avoiding complex entanglement between multiple adapters and improving controllability and stability. Extensive experiments show that our method delivers competitive quantitative performance and significantly improved perceptual quality. Code is available at https://github.com/Yvan1001/AnyStyle.

ECCV 2026

Reward Lightning: Fast Video Generation via Homologous Preference Distillation

Jiaxiang Cheng, bing ma, Xuhua Ren, Kai Yu, Peng Zhang, Tianxiang Zheng, Qinglin Lu

Achieving simultaneous preference alignment and distillation acceleration in video diffusion models remains an open challenge. Existing methods optimize the two objectives over mismatched representation spaces, where improving one objective often compromises the other. To overcome this, we propose Reward Lightning, a unified framework that aligns and accelerates a video diffusion model within a single shared representation. Its central principle is homology: both objectives are evaluated on identical latent features, which mitigates the gradient conflicts that arise when they are optimized over disjoint representations. As a foundational component, we first introduce a latent reward model (LRM) that scores videos directly in the latent space, without decoding back to the pixel space. Building on the LRM, homologous preference distillation (HPD) reuses this shared backbone to perform adversarial distillation and preference alignment jointly, yielding few-step generators that remain faithful and well aligned. Extensive experiments demonstrate that the LRM surpasses pixel-level and latent-level reward baselines by 11.0% and 14.7% in preference accuracy, and that Reward Lightning generates high-fidelity videos in merely 1 to 4 steps, improving the average VBench score by 2.1% while leading in text alignment, motion quality, and visual quality. Project page: https://reward-lightning.github.io.

ECCV 2026

GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis

Yishen Liu, Hongchang Chen, Pengcheng Zhao, Yunfan Bao, Yuxi Tian, Jieming Zhang, Hao Chen, Zhi Zheng, Yongchun Liu, Ying Li, Dongpu Cao

The performance of visual anomaly inspection in industrial quality control is often constrained by the scarcity of real anomalous samples. Consequently, anomaly synthesis techniques have been developed to enlarge training sets and enhance downstream inspection. However, existing methods either suffer from poor integration caused by inpainting or fail to provide accurate masks. To address these limitations, we propose GroundingAnomaly, a novel few-shot anomaly image generation framework. Our framework introduces a Spatial Conditioning Module that leverages per-pixel semantic maps to enable precise spatial control over the synthesized anomalies. Furthermore, a Gated Self-Attention Module is designed to inject conditioning tokens into a frozen U-Net via gated attention layers. This carefully preserves pretrained priors while ensuring stable few-shot adaptation. Extensive evaluations on the MVTec AD and VisA datasets demonstrate that GroundingAnomaly generates high-quality anomalies and achieves state-of-the-art performance across multiple downstream tasks, including anomaly detection, segmentation, and instance-level detection. Code will be made publicly available from this URL.

ECCV 2026

PASDiff: Physics-Aware Semantic Guidance for Joint Real-world Low-Light Face Enhancement and Restoration

Yilin Ni, Wenjie Li, Zhengxue Wang, Juncheng Li, Guangwei Gao, Jian Yang

Face images captured in real-world low light suffer multiple degradations—low illumination, blur, noise, and low visibility, etc. Existing cascaded solutions often suffer from severe error accumulation, while generic joint models lack explicit facial priors and struggle to resolve clear face structures. In this paper, we propose PASDiff, a Physics-Aware Semantic Diffusion in a training-free manner. To achieve a plausible illumination and color distribution, we leverage inverse intensity weighting and Retinex theory to introduce photometric constraints, thereby reliably recovering visibility and natural chromaticity. To faithfully reconstruct facial details, our Style-Agnostic Structural Injection (SASI) extracts structures from an off-the-shelf facial prior while filtering out its intrinsic photometric biases, seamlessly harmonizing identity features with physical constraints. Furthermore, we construct WildDark-Face, a real-world benchmark of 700 low-light facial images with complex degradations. Extensive experiments demonstrate that PASDiff significantly outperforms existing methods, achieving a superior balance among natural illumination, color recovery, and identity consistency. Code and dataset will be available at https://github.com/IVIPLab/PASDiff.

ECCV 2026

Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction

Efstathios Karypidis, Spyros Gidaris, Nikos Komodakis

Accurate future video prediction requires both high visual fidelity and consistent scene semantics, particularly in complex dynamic environments such as autonomous driving. We present Re2Pix, a hierarchical video prediction framework that decomposes forecasting into two stages: semantic representation prediction and representation-guided visual synthesis. Instead of directly predicting future RGB frames, our approach first forecasts future scene structure in the feature space of a frozen vision foundation model, and then conditions a latent diffusion model on these predicted representations to render photorealistic frames. This decomposition enables the model to focus first on scene dynamics and then on appearance generation. A key challenge arises from the train-test mismatch between ground-truth representations available during training and predicted ones used at inference. To address this, we introduce two conditioning strategies, nested dropout and mixed supervision, that improve robustness to imperfect autoregressive predictions. Experiments on challenging driving benchmarks demonstrate that the proposed semantics-first design significantly improves temporal semantic consistency, perceptual quality, and training efficiency compared to strong diffusion baselines. We provide the implementation code at https://github.com/Sta8is/Re2Pix.

ECCV 2026

EventSTU: Event-Guided Efficient Spatio-Temporal Understanding for Video-LLMs

Wenhao XU, Xin Dong, Yue Li, Haoyuan Shi, Yueyi Zhang, Zhiwei Xiong

Video large language models have demonstrated strong video understanding capabilities, but suffer from high inference costs due to the massive number of tokens in long videos. Inspired by efficient biological vision systems, we propose EventSTU, a training-free framework guided by bio-inspired event cameras to significantly reduce redundant tokens, thereby enabling efficient spatio-temporal understanding. In the temporal domain, we design a coarse-to-fine keyframe sampling algorithm that exploits the change-triggered property of event cameras to eliminate redundant frames. In the spatial domain, we design an adaptive token pruning algorithm that leverages the visual saliency of events as a zero-cost prior to guide token reduction. From a holistic spatio-temporal perspective, we further integrate question relevance from keyframe sampling to adaptively allocate token retention budgets. To facilitate evaluation, we construct EventBench, the first event-inclusive, human-annotated multimodal benchmark that covers diverse real-world scenarios. Beyond physical event cameras, EventSTU also supports general video understanding through simulated events. Comprehensive experiments show that EventSTU achieves 2.87× FLOPs reduction and 3.10× prefilling speedup over the strongest baseline while still improving performance.

ECCV 2026

DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models

Mingue Park, Prin Phunyaphibarn, Phillip (Yuseung) Lee, Minhyuk Sung

We introduce DiverseVAR, a test-time framework that enhances the output diversity of text-conditioned visual autoregressive models (VAR) without additional training or substantial computational overhead. While VAR models have recently emerged as strong competitors of diffusion and flow models for image and video generation, they suffer from a critical diversity limitation: even simple prompts often produce nearly identical outputs. This issue has largely gone unnoticed amid the field’s predominant focus on image quality. We address this limitation in two stages. First, we systematically explore diversity enhancement techniques by injecting noise into different components of VAR at test time, finding that noise injection into the text embedding yields the best diversity gains. However, this comes at the cost of sharp degradation in image quality. To recover quality, we propose scale-travel: a latent refinement technique that leverages a multi-scale autoencoder to extract coarsescale tokens to resume generation from intermediate stages. Extensive experiments demonstrate that combining text-embedding noise injection with scale-travel refinement substantially improves diversity while minimizing quality degradation, advancing the diversity-quality Pareto front. Project page: https://diverse-var.github.io/

ECCV 2026

BackTranslation2.0 - A Linguistically Motivated Metric to Assess Sign Language Production

Oliver Cory, Maksym Ivashechkin, Oline Ranum, Jian He Low, Edward Fish, Anton Pelykh, Karahan Sahin, Ozge Mercanoglu Sincan, Richard Bowden

Sign Languages (SLs) are the primary means of communication for millions of deaf1 individuals, yet existing evaluation metrics for generated SL remain simplistic and poorly aligned with human judgements. We introduce BackTranslation2.0, a linguistically grounded evaluation metric for text-to-sign translation that moves beyond naïve backtranslation. Our approach adopts an agentic framework in which a deterministic pipeline orchestrates a suite of specialised tools to assess four scoring dimensions—grammatical correctness, phonological accuracy, motion fluency, and generation fidelity—aligned with human rater assessments. Tool outputs are not treated independently: a set of large language model (LLM)-based cross-referential comparison modules evaluates consistency across tools and checks outputs against linguistic expectations, enabling structured reasoning over grammatical, phonological, and motion-level evidence. Final dimension scores are computed through deterministic weighted formulas over validated tool outputs. To validate BackTranslation2.0, we introduce and evaluate on a British Sign Language (BSL) dataset rated in a human rater study across the same quality dimensions, following a protocol developed in collaboration between linguists and deaf experts, benchmarking against six baseline metrics. Our method demonstrates strong correlation with human judgements across all dimensions, providing a more comprehensive, interpretable, and linguistically principled evaluation framework for sign language production systems.

ECCV 2026

Enlightening Photographic Style Transfer with a Self-Supervised Photographic Embedding

Chengxuan Zhu, Jiacong Fang, Shuchen Weng, Youwei Lyu, Jiajun Tang, Qingnan Fan, Chao Xu, Boxin Shi

Photographic style transfer aims to render a content imagewith the retouching style of a reference photo while preserving scenestructure and texture. Unlike artistic style, photographic style is dom-inated by subtle, continuous tone/color changes and smooth spatial ef-fects, which are difficult to represent with semantics-oriented or text-supervised image embeddings. We propose PETAL, a PhotographicEmbedding for Transfer with an Adaptive LUT. PETAL learns a dedi-cated photographic embedding through self-supervised photographic styleaugmentation and uses it to condition a lightweight adaptive 5D neu-ral LUT. The learned embedding outperforms prior representations inphotographic style retrieval, and the full framework enables reference-based style transfer without test-time optimization, achieving strongerquantitative and perceptual performance than state-of-the-art baselines.Project page: https://petal-pku.pages.dev/.

ECCV 2026

What is the Right Embedding Space for Contrastive Learning in Referring Expression Counting?

Kostas Triaridis, Panagiotis Kaliosis, E-Ro Nguyen, Jingyi Xu, Dimitris Samaras, Hieu Le

Referring Expression Counting (REC) requires distinguishing visually similar objects described by fine-grained text cues. Existing methods tackle this via image-text contrastive learning of visual features which aims to distinguish visual features corresponding to positive and negative referring expressions. However, this strategy relies heavily on accurate image–text alignment and is constrained by the limited number of available negative expressions. We argue that these limitations can be avoided by performing contrastive learning entirely in the visual embedding space. To this end, we introduce C-REX, a simple but effective supervised contrastive learning framework that learns fine-grained visual representations by contrasting visual tokens within the same image. By shifting the negative samples from incorrect Referring Expressions to visual tokens from the image, C-REX vastly increases the number of potential negatives, providing richer and more stable supervision. This design leads to stronger fine-grained visual discrimination and better generalization across complex open-world counting settings. C-REX serves as a plug-in module that can be seamlessly applied to any existing REC model without architectural changes. We apply it in three different REC architectures and achieve state-of-the-art results improving by up to 28% in MAE and 24.5% in RMSE. Moreover, we show that our framework is versatile and general, and can be applied to other counting tasks like class-agnostic counting, improving the performance of existing models.

ECCV 2026

Video Can Teach PAN-Sharpening: PSF-Aware Cross-Domain Supervision

Hyun-Ho Kim, Munchurl Kim, Jaehyup Lee

Most PAN-sharpening networks are trained on synthetically degraded panchromatic (PAN) and multispectral (MS) pairs because real high-resolution MS (HRMS) labels are rarely available. However, real satellite acquisitions often suffer from cross-modal misregistration and sensor-dependent optical responses that are absent from synthetic training pipelines, leading to spectral distortions and double-edge artifacts at test time. This suggests that the key bottleneck lies not only in network design but also in the supervision paradigm. In this work, we show that natural videos can serve as a spatial teacher for PAN-sharpening: nearby HR frame pairs provide abundant high-frequency structures and exhibit frame-to-frame shifts that mimic realistic misregistration, enabling strong spatial supervision without HRMS labels. Based on this insight, we propose ViPS, a cross-domain training framework under the paradigm of “Video can teach PAN-Sharpening”. ViPS disentangles supervision sources by learning spatial detail restoration from video-derived pseudo PAN–MS pairs, while enforcing spectral fidelity from real satellite PAN–MS pairs. To align optical degradations across domains, we utilize point spread function (PSF) banks and randomly sample a kernel during training, enabling physically grounded cross-domain learning. Extensive experiments across multiple sensors show that ViPS outperforms very recent state-of-the-art methods under both reducedand full-resolution settings, while maintaining fast inference.

ECCV 2026

CHARTSTYLE-100K: A Large-Scale Dataset for Structured Visualization Style Transfer

Yuwei Yang, Tianchi Xie, Jinhong Ni, Yukai Guo, Jing Zhang, Liang Zheng, Yalong Bai, Yuhui Yuan

Given an input image, style transfer aims to recast it into the style of a reference image while preserving its content. While this problem is well addressed for natural images, we find that structured images such as charts, diagrams and tables demand capabilities fundamentally distinct from natural image stylization, i.e. visual elements encode data through geometry (bar heights, arc angles, point positions), and dense text must be preserved exactly. We term this structured style transfer as changing appearance while keeping strict element-level fidelity. Even frontier models like GPT-Image-1.5 and Nano-Banana-Pro often struggle here, producing inconsistent transfer, structural distortion, and content leakage. The obvious fix, training on data that restyles a content chart toward a reference, inherits these failures: it distorts the content and leaks the reference into the target. We introduce ChartForge, a data pipeline that instead builds triplets in reverse: starting from a style image, it produces a target of similar style but different content, then a matching content chart. Because the content is derived from the target, the two stay structurally are disentangled from the start, avoiding forwardpipeline leakage. With style-space resampling and multi-dimensional filtering, ChartForge yields ChartStyle-100K, over 100K triplets across 36 chart types and 3 visualization families. For evaluation, we further build ChartStyle-Bench, a benchmark of 300 content-style pairs with 6 metrics. Using ChartStyle-100K, Training ReChart by progressively fine-tuning Qwen-Image-Edit on ChartStyle-100K achieves the best overall performance across all baselines, including GPT-Image-1.5 and Nano-Banana-Pro.

ECCV 2026

From Minimal Clinical Prompts to 3D: Spacing-Aware Prompt Propagation for Multimodal Prostate Lesion Segmentation in bpMRI

Jiacheng Wang, Heinrich von Busch, Robert Grimm, Ipek Oguz, Dorin Comaniciu, Ali Kamen, Bin Lou

Accurate 3D delineation of prostate cancer lesions in biparametric MRI (bpMRI) supports targeted biopsy and therapy planning, yet clinical reports typically provide only sparse 2D measurements or a single contour. We present SAPP (Spacing-Aware Prompt Propagation), a prompt-to-volume adaptation of SAM 2 that converts one clinical prompt on a single slice (diameter, box, circle, or contour) into a coherent 3D lesion mask from multimodal bpMRI. SAPP couples adaptive multimodal fusion for accurate image-level prompted segmentation with a spacing-aware volume-level prompt propagation module. The propagation uses spacing-decayed, confidence-gated streaming-memory attention and a learned stop rule to prevent leakage in anisotropic volumes. Trained on 3,256 scans from 7 institutions, SAPP generalizes to 14 held-out cohorts (3,504 fully annotated scans plus 516 routine-care weak-label studies), achieving 0.84/0.81 DSC on the prompted slice (box/diameter) and 0.86/0.78/0.76 DSC for volumetric masks (oracle-mask/box/diameter), consistently outperforming baselines while reducing over-propagation. Beyond segmentation, SAPP can accelerate annotation workflows and enable scalable generation of high-quality volumetric lesion masks, supporting the development of robust clinical AI models.

ECCV 2026

3D Gaussian Texture for Real-time Mesoscale Appearance Synthesis and Rendering

Xiang Chen, Jia Li, Lu Wang, Beibei Wang

We propose a novel framework for modeling, synthesis, and rendering of mesoscale appearances based on 3D Gaussian Splatting (3DGS) for better quality and performance. The key problem is that synthesis of 3D Gaussians is difficult due to their explicit nature. Therefore, the core of our method is a multi-layer Gaussian texture representation to support high-quality and efficient synthesis of 3D Gaussians. Each layer of the texture contains Gaussians with similar heights to the object surface, which is compatible with 2D image texture synthesis approaches. Through by-example autocovariance-preserving synthesis, we achieve the run-time synthesis of mesoscale appearances with their structures preserved. Due to the deformation of mapping from texture to arbitrary objects and the baking of lighting, directly applying the Gaussian texture for rendering may cause visual artifacts. Thus, we further introduce two components: a deformation-aware mapping and a material-lighting decomposition. The mapping is based on barycentric UV coordinates with a scaling constraint of Gaussians to reduce artifacts. The decomposed materials are represented by latent features to maintain the consistency of the material attributes during synthesis. Consequently, our approach achieves a faithful representation of mesoscale appearances modeled from multi-view images, which supports synthesis and rendering on arbitrary objects in real time, achieving about 500× acceleration compared to the latest NeRF-Texture. Our code is available at: https://github.com/ChenXiang0810/multilayer-gaussian-texture.

ECCV 2026

ASSCG: Just-Right Gating over Chattering for Fast–Slow LLM Planning in Autonomous Driving

Sining Ang, Yuan Chen, Liu Haiyan, Xuanyao Mao, Jason Bao, Xuliang Xuliang, Bingchuan Sun, Yan Wang

Large language models (LLMs) can improve autonomous driving planning but are costly to query online, and existing fast–slow planners often rely on hand-designed triggering rules that either over-call the slow system or call it at the wrong times. We formulate slow-system invocation as a resource-aware sequential decision problem and propose the Adaptive Slow-System Control Gate (ASSCG), which makes framelevel Query/Cache/Drop decisions to refresh, reuse, or suppress slow guidance. ASSCG uses an RWKV backbone for efficient long-horizon gating and is trained with supervised fine-tuning followed by GRPOstyle compute-aware reinforcement fine-tuning. We apply ASSCG to two different fast–slow architectures: (i) AsyncDriver on nuPlan Hard20 closed-loop evaluation, where ASSCG improves score to 67.28 (+2.28) while reducing average end-to-end inference latency by ∼60%; and (ii) a RecogDrive-based dual system that we build by replacing its original VLM-2B module with a lightweight ViT-based fast planner and adding an LLM slow planner, evaluated on NAVSIM, where ASSCG achieves 91.4 PDMS (+0.6) and increases average speed by ∼25%. The project page, including video visualizations and additional results, is available at https://williamxuanyu.github.io/asscg/.

ECCV 2026

Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration

Kyoleen Kwak, Daeho Kim, Jeong Woon Lee, Hyoseok Hwang

Accurate LiDAR-camera calibration is essential for robust multi-modal perception. Targetless approaches avoid manual setup but remain limited by the scarcity of discriminative cross-modal features. Recent methods address this by reconstructing the scene within a differentiable model, enabling extrinsic optimization through dense photometric supervision. Among these, 3D Gaussian Splatting (3DGS) has been widely adopted as a geometric proxy that bridges LiDAR and camera within a single differentiable framework. However, since 3DGS was originally designed for novel view synthesis, existing methods tend to prioritize rendering quality, causing the proxy geometry to drift from the true LiDAR structure. We propose a framework that preserves the metric geometry of the Gaussian proxy by aggregating multi-view LiDAR observations for dense depth supervision and blocking photometric gradients from updating the Gaussian spatial parameters. We validate our method on public driving datasets, where it consistently outperforms existing targetless methods in calibration accuracy. Code and dataset are available at https://github.com/AIRLABkhu/GeoP-Calib

ECCV 2026

OmniColor: A Unified Framework for Multi-modal Lineart Colorization

Xulu Zhang, Haoqian DU, Xiao-yong Wei, Li Qing

Lineart colorization is a critical stage in professional content creation, yet achieving precise and flexible results under diverse user constraints remains a significant challenge. To address this, we propose OmniColor, a unified framework for multi-modal lineart colorization that supports arbitrary combinations of control signals. Specifically, we systematically categorize guidance signals into two types: spatially-aligned conditions and semantic-reference conditions. For spatially-aligned inputs, we employ a dual-path encoding strategy paired with a Dense Feature Alignment loss to ensure rigorous boundary preservation and precise color restoration. For semantic-reference inputs, we utilize a VLM-only encoding scheme integrated with a Temporal Redundancy Elimination mechanism to filter repetitive information and enhance inference efficiency. To resolve potential input conflicts, we introduce an Adaptive Spatial-Semantic Gating module that dynamically balances multi-modal constraints. Experimental results demonstrate that OmniColor achieves superior controllability, visual quality, and temporal stability, providing a robust and practical solution for lineart colorization. The source code is available at https://github.com/zhangxulu1996/OmniColor.

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