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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

CausalDrive: Real-time Causal World Models for Autonomous Driving

Tianyi Yan, Huan Zheng, Dubing Chen, Meizhi Qu, Yingying Shen, Lijun Zhou, Mingfei Tu, Bing Wang, Guang Chen, Hangjun Ye, Haiyang Sun, Cheng-zhong Xu, Shen Jianbing

World models have emerged as a promising paradigm for scaling autonomous driving (AD) data, yet existing video generative models fall short as interactive simulators. Layout-conditioned renderers rely on "oracle" future trajectories of all background agents, rendering them strictly non-reactive. Conversely, pure action-conditioned predictors lack semantic control over complex interactions and suffer from prohibitive diffusion latencies, hindering closed-loop policy learning. To bridge this gap, we present CausalDrive, a controllable, real-time foundation driving world renderer. CausalDrive operates solely on the initial front-view frame, the ego-vehicle’s trajectory, and a macroscopic text prompt. By excluding future NPC layouts, we compel the model to intrinsically predict causal interactions, enabling text-driven control over Driving Sociology—allowing users to dynamically orchestrate diverse counterfactual reactions to identical ego-actions. To overcome the efficiency bottleneck and address the covariate shift in autoregressive generation, we propose a novel Context-Forced DMD architecture. This combines continuous flow-matching with a self-correcting distillation objective, achieving interactive speeds of 12 FPS. This breakthrough transforms the passive video generator into a playable neural simulator. We demonstrate its versatility across three downstream applications: (1) generative closed-loop evaluation with significantly mitigated collision artifacts, (2) large-scale Reinforcement Learning (RL) post-training driven by a Video2Reward module, and (3) real-time human-in-the-loop simulation. Extensive experiments validate that policies trained within CausalDrive’s reactive scenarios exhibit superior interaction capabilities in the real world.

ECCV 2026

TiltDiff: Tilted Weight-Space Diffusion for Neural Network Generation

En-Ni Chuang, Hanjuan Huang, Hao-Jia Song, Hsing-Kuo Kenneth Pao, Tyng-Luh Liu

Generating functional neural-network weights from trained model collections is a central problem in weight-space learning. We introduce TiltDiff, a performance-tilted latent diffusion framework for neural-network generation. TiltDiff tokenizes network weights, encodes them into a compact latent space with a Transformer autoencoder, and uses a U-Net-based diffusion model to synthesize latent representations that decode into functional parameters. To favor stronger models, we weight the denoising loss by validation accuracy, biasing the learned distribution toward higher-performing regions of weight space. Experiments show that TiltDiff improves predictive performance, robustness to random parameter masking, and representational diversity over prior weight-generation methods. We further combine diffusion U-Net connectivity with attribution analysis to identify class-specific decision pathways. These pathways exhibit emergent correspondence across independently generated models despite differing raw parameters, and pathwaylevel masking verifies their importance for target-class prediction. Our results show that performance-tilted diffusion generates accurate, robust, diverse, and structurally interpretable neural-network weights.

ECCV 2026

Syn4D: A Multiview Synthetic 4D Dataset

Zeren Jiang, Yushi Lan, Yihang Luo, Yufan Deng, Zihang Lai, Edgar Sucar, Christian Rupprecht, Iro Laina, Diane Larlus, Chuanxia Zheng, Andrea Vedaldi

Progress in tasks like 3D reconstruction and tracking of dynamic scenes from monocular video is constrained by the scarcity of high-quality datasets with dense, complete, and accurate geometric annotations. To address this limitation, we introduce Syn4D, a multiview synthetic dataset of dynamic scenes that includes ground-truth camera motion, depth maps, dense tracking, and parametric human pose annotations. A key feature of Syn4D is the ability to unproject any pixel into 3D to any time and to any camera. We conduct extensive evaluations across multiple downstream tasks to demonstrate the utility and e!ectiveness of the proposed dataset, including 4D scene reconstruction, 3D point tracking, geometry-aware camera retargeting, and human pose estimation. The experimental results highlight Syn4D’s potential to facilitate research in dynamic scene understanding and spatiotemporal modeling.

ECCV 2026

Denoised Variance-Based Pruning with Optimal Brain Bias Compensation

Geon Tack Lee, Choo Jaegul, Kang Eun Jeon

Vision Transformers (ViTs) achieve state-of-the-art performance but carry massive computational overhead that restricts edge deployment. Although structural pruning has emerged as a key strategy to reduce these costs, existing methods often suffer from severe accuracy degradation or require expensive retraining. Recently, Variance-Based Pruning (VBP) introduced a promising paradigm by selecting neurons based on activation variance; however, it remains limited by statistical noise in finite-sample activation covariance and reliance on bias-only updates that cannot fully account for structural reconstruction error. To address these limitations, we introduce Denoised Variance-Based Pruning with Optimal Brain Bias Compensation (DVBP + OB2C). We leverage random matrix theory to filter noise from the activation covariance spectrum for robust neuron selection and mathematically prove that integrating mean-shift compensation into the Optimal Brain Compression objective reduces the layer-wise Hessian exactly to the activation covariance matrix. This enables an optimal, closed-form update of the remaining weights using the same statistics gathered for selection. Extensive experiments on DeiT, Swin, and ConvNeXt architectures demonstrate that DVBP + OB2C achieves state-of-the-art training-free performance; at 50% MLP pruning, it retains over 90% of the original Top- 1 accuracy on Small and Base variants, outperforming VBP by up to 29.46% (ConvNeXt-T) and 7.33% (Swin-S). The code is available at: https://github.com/geontackee/DVBP_OB2C.

ECCV 2026

Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows

Janne Perini, Rafael Bischof, Moab Arar, Ayça Duran, Michael Kraus, Siddhartha Mishra, Bernd Bickel

Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost makes extensive design exploration impractical. We introduce WinDiNet (Wind Diffusion Network), a pretrained video diffusion model that is repurposed as a fast, differentiable surrogate for this task. Starting from LTX-Video, a 2Bparameter latent video transformer, we fine-tune on a dataset of 13,000 2D incompressible CFD simulations over procedurally generated building layouts. A systematic study of training regimes, conditioning mechanisms, and VAE adaptation strategies, including a physics-informed decoder loss, identifies a configuration that outperforms purpose-built neural PDE solvers. The resulting model generates full 112-frame rollouts in under a second. As the surrogate is end-to-end differentiable, it doubles as a physics simulator for gradient-based inverse optimization: given an urban footprint layout, we optimize building positions directly through backpropagation to improve wind safety as well as pedestrian wind comfort. Experiments on singleand multi-inlet layouts show that the optimizer discovers effective layouts even under challenging multi-objective configurations, with all improvements confirmed by ground-truth CFD simulations.

ECCV 2026

High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control

Junha Kim, Hyunjoon Park, Donghyeon Cho

Image outpainting extends an image beyond its original borders, requiring seamless style integration and globally coherent scene completion. Building on the success of diffusion models, recent methods have achieved substantial improvements in visual quality. In practice, however, high-resolution outpainting is commonly performed via progressive expansion around a fixed source image, particularly in artwork scenarios. Despite this progress, existing approaches still suffer from three key limitations: (i) the absence of a reliable global planning mechanism, which leads to structural instability and error accumulation at high resolutions; (ii) limited spatial controllability beyond text prompts, making it difficult to place objects at user-specified locations; and (iii) high inference latency caused by inherently sequential patch generation. To address these issues, we propose a global blueprint-guided two-stage diffusion framework for layout-controllable high-resolution outpainting with efficient parallel synthesis. In Stage 1, we generate a low-resolution global blueprint using a layout adapter that injects bounding-box conditions into a Stable Diffusion inpainting backbone, producing a globally consistent structural plan while extracting global guidance features. In Stage 2, we synthesize high-resolution local patches in parallel by injecting the blueprint-derived global guidance and initializing each patch from the blueprint using the low-frequency preservation property of forward diffusion. This design eliminates sequential dependency while maintaining global coherence. Extensive experiments on large-scale artwork datasets demonstrate improved visual fidelity, stronger semantic consistency, and substantially reduced inference time compared to prior baselines, while uniquely supporting explicit layout control for artwork outpainting. Our code will be available at https://github.com/poohoh/BlueOut.

ECCV 2026

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators

Jintao Rong, Xin Xie, Xinyi Yu, Linlin Ou, Xinyu Zhang, Chunhua Shen, Dong Gong

Training-free motion customization imposes motion patterns from reference videos onto video generators through test-time computation. Most existing methods target full diffusion models, requiring many denoising steps and high computational cost. With the rise of efficient distilled models, a natural question arises: can test-time motion customization be applied directly to distilled generators with their accelerated sampling and efficiency gains? However, our analysis reveals that existing training-free techniques fail on distilled models. Distillation fundamentally alters the denoising dynamics that prior test-time guidance relies on, and the large denoising steps of distilled generators discard the dense intermediate states that score guidance requires, rendering existing motion control strategies incompatible with fast generation. To address this limitation, we propose MotionEcho, a novel training-free test-time distillation framework that enables motion customization for distilled video generators. The key idea is to correct the student model’s sampling trajectory with restricted usage of a high-quality diffusion teacher at inference time. Teacher supervises the student’s denoising by re-noising the student’s endpoint onto its dense trajectory to form a motion-aligned clean endpoint, then interpolating it with the student’s, while an adaptive scheduling mechanism determines when and how much teacher guidance is needed. As a result, MotionEcho restores generative trajectories for distilled video generators via lightweight, adaptive test-time teacher guidance, enabling accurate motion control without compromising generation efficiency. Extensive experiments on multiple distilled video generation models demonstrate that our method significantly improves motion fidelity and visual quality while retaining the efficiency advantages of distilled generation. Project page: MotionEcho.

ECCV 2026

BIP: Bi-level Information Transfer and Completion Prompting for Visual Recognition with Missing Modalities

Haoran Fan, XU HAN, Xianglong Bao, Zheng Gao, Qi Fan, Yang Song, Jiaojiao Jiang

Existing multimodal visual recognition methods addressing missing modalities have achieved great progress, mainly based on the feature reconstruction framework. However, feature reconstruction often introduces distribution inconsistency between synthesized and real features. Considering each modality as a different view of the same instance, each view is noisy and incomplete, but important representations, such as semantic details, tend to be shared between all modalities. Thus, we propose that AlignPrompt alleviates the distribution inconsistency problem by completing and aligning the semantic representation between synthesized and non-missing prompts. AlignPrompt is an instance-level prompt, implemented via a modal-shared-specific structure including a modalshared factor and a modal-specific factor. Specifically, for each modality pair, AlignPrompt aligns modality-specific factors of all modalities in a shared space by the modal-shared factor as an anchor, and completes the semantic representation by maximizing the mutual information between the synthesized factor and the non-missing factor. Besides, we also propose HyperPrompt to globally regulate the association of prompts of each layer based on different modalities missing scenarios: it transfers global prompts across layers by the inter-layer modules generated from an independent network. This collaborative prompts framework achieves state-of-the-art performance on multiple multimodal classification datasets while maintaining parameter efficiency.

ECCV 2026

Learning Global Camera Poses from Noisy View-Graphs for Structure from Motion

Fadi Khatib, Meirav Galun, Ronen Basri

Camera pose estimation is a key step in 3D reconstructionand view-synthesis pipelines. We present a deep, global Structure-from-Motion framework based on learned view-graph aggregation. Our methodemploys a permutation-equivariant, edge-conditioned graph neural net-work that takes noisy pairwise relative poses as input and outputs glob-ally consistent camera extrinsics. The network is trained without ground-truth supervision, relying solely on a relative-pose consistency objective.This is followed by 3D point triangulation and robust bundle adjust-ment. Our approach is efficient, scalable to more than a thousand images,and robust to graph density. We evaluate our method on MegaDepth,1DSfM, Strecha, and BlendedMVS. These experiments demonstrate thatour method achieves superior rotation and translation accuracy com-pared to deep track-centric methods while registering more images acrossmany scenes, and competitive results compared to state-of-the-art clas-sical pipelines, while being much faster.

ECCV 2026

NAPA: Natively Multimodal Autoregressive Perception Architecture

Phuc Le Khac Hong, Yasser Dahou, Sanath Narayan, Ankit Singh, Wamiq Para, Ngoc Dung Huynh, Sofian Chaybouti, Hakim Hacid

Perception-centric systems are typically implemented with a modular encoderdecoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module) for task prediction. We ask whether this separation is necessary for tasks such as openvocabulary segmentation and OCR. We present Falcon Perception, a single, early-fusion dense Transformer that processes image patches, text, and task tokens in a single shared parameter space. Falcon Perception uses a hybrid attention mask: bidirectional for image tokens and causal for text/task tokens, and keeps dense outputs practical by emitting only a small number of task tokens per instance and decoding continuous spatial outputs with lightweight heads, enabling parallel high-resolution mask prediction. On SA-Co, Falcon Perception achieves 68.0 F , compared to 62.3 for a strong state-of-the-art promptable segmentation baseline. 1 To measure compositional grounding beyond saturated referring benchmarks, we introduce PBench and show large gains on spatial and dense long-context regimes (e.g., 53.5 vs. 31.6 on spatial and 72.6 vs. 58.4 on Dense). Finally, we extend the same architecture to document OCR: a compact Falcon-OCR model with 300M parameters achieves 80.3% on olmOCR and 88.64% on OmniDocBench. Code: GitHub, PBench: HF Dataset, Blog: Project Blog.

ECCV 2026

Horizon3D: Sparse Radar-Camera Fusion for Long-Range 3D Perception in Autonomous Driving

Geonho Bang, Geunju Baek, Dongyoung Lee, Wonjun Jeong, Jun Won Choi

Long-range 3D object detection is critical for safe autonomous driving at highway speeds, yet existing radar-camera fusion methods face notable limitations at extended ranges. BEV-based approaches effectively encode scene-level context but incur rapidly growing computational cost and struggle to preserve fine-grained object-level detail, while querybased methods provide efficient object-centric encoding but lack sufficient scene-level context. Temporal fusion introduces additional challenges: distant objects produce only a few radar returns and occupy only a few image pixels, requiring scene-level accumulation, while high-speed motion causes large inter-frame displacements that require object-level motion modeling. BEV-based aggregation alleviates sparsity through multi-frame accumulation but is less suited to individual object motion, whereas query-based modeling captures object-level motion but provides limited scene-level temporal context. In this paper, we propose Horizon3D, a sparse radar-camera fusion framework for long-range 3D object detection that jointly captures object-level detail and scene-level context in both spatial and temporal dimensions through a hybrid representation that combines Gaussian primitives with sparse BEV features. Horizon3D first employs Keypoint-Guided Gaussian Initialization (KGGI) to initialize Gaussian primitives at object keypoints estimated from radar and camera features. Object-Centric Sparse Fusion (OCSF) aggregates cross-modal features around these primitives and splats the refined Gaussians onto the BEV plane, where they are fused with sparse radar BEV features to combine object-level detail with scene-level context. Finally, Dual-Path Temporal Fusion (DPTF) aggregates temporal cues through a BEV path for multi-frame feature accumulation and a Gaussian path for propagating primitives across frames to encode per-object motion. Extensive evaluations on TruckScenes demonstrate that Horizon3D achieves stateof-the-art performance for radar-camera 3D object detection. On the validation set, our approach outperforms the previous best method by +3.0 NDS and +1.6 mAP while maintaining a sparse representation with competitive inference speed.

ECCV 2026

ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation

Hanxiao Sun, Mingxin Yang, Shuhui Yang, Zebin He, Xintong Han, Hongbo Fu, Chunchao Guo, Wenhan Luo

Single-image-to-3D generative models can now produce highquality geometry, yet conditioning on a single view inevitably introduces ambiguity about unseen regions. Multi-view conditioning can reduce this ambiguity, but existing methods either require fixed canonical viewpoints or rely on external reconstruction modules that impose heavy training costs and limit generation quality. We observe that pretrained singleview models already possess strong 2D-to-3D grounding that can be reused for multi-view conditioning. However, a closer analysis reveals that their conditioning mechanism entangles orientation control with geometry transfer, two functions that conflict when images from different viewpoints are naively combined. Based on this analysis, we propose ROAR-3D, a lightweight method that upgrades a pretrained singleview model to accept an arbitrary number of unposed images. A tokenwise view router assigns each 3D latent token to its most relevant view, implicitly establishing 2D-to-3D correspondences without explicit pose input. A dual-stream attention design preserves the pretrained primaryview behavior while routing auxiliary views through a separate path dedicated to geometric enrichment. An orientation perturbation strategy ensures the auxiliary path learns orientation-independent geometry transfer. These components introduce lightweight architectural additions and incur only low additional inference overhead relative to the singleview baseline. ROAR-3D achieves state-of-the-art multi-view 3D generation quality and supports test-time view scaling from 1 to 12+ views with consistent improvements.

ECCV 2026

FLM-Occ: Feed-forward Likelihood Maximization for Efficient Indoor Occupancy Prediction

Guangcheng Chen, Lihuang Fang, Huaqi Tao, Yicheng He, Li He, Zhang Hong

Recent indoor occupancy prediction methods adopt Gaussian primitives as a sparse 3D representation for computational efficiency. However, their training relies on voxel classification, which imposes only local constraints and lacks global supervision on the distribution of the primitives. Therefore, they inevitably predict spurious primitives in empty regions, undermining both representational and computational efficiency. To address this, we propose Feed-forward Likelihood Maximization (FLM), a novel framework that reformulates occupancy prediction as voxel distribution estimation. In FLM, a network is trained to predict a mixture model that maximizes the likelihood over groundtruth occupied voxels in a feed-forward manner. To enable end-to-end training of networks and voxelization of a standard mixture model, we define mixture weights as normalized primitive volumes to implicitly enforce simplex constraints and derive novel voxelization formulas. Based on FLM, our FLM-Occ, a novel method that is capable of relocating randomly initialized primitives over long distances to model a scene. On Occ-ScanNet, FLM-Occ achieves superior accuracy using only 32 superquadrics, 2.7% of the prior SoTA, while running 3.7× faster.

ECCV 2026

TEASR: Training-Efficient Any-Step Diffusion Transformer for Real-World Image Super-Resolution

Xiang Gao, Chenxin Zhu, Yushun Fang, Qiang Hu, Xiaoyun Zhang

Diffusion models excel in Real-World Image Super-Resolution (Real-ISR) due to their powerful generative priors but suffer from slow iterative sampling. Although existing one-step distillation methods accelerate inference, they typically require auxiliary teacher models that inflate training memory and restrict scalability to large-scale architectures. Furthermore, these fixed-step models lack the flexibility to trade off speed for quality. In this paper, we propose TEASR, a training-efficient anystep diffusion framework for Real-ISR that enables both one-step and multi-step restoration within a unified model. Our key idea is to perform self-adversarial distillation within a single diffusion model, eliminating the need for auxiliary teachers or discriminators. Specifically, we propose a timestep-aware rectification strategy that stabilizes one-step generation across noise levels. These two designs further enables the distillation of 20B-parameter diffusion models on a single GPU, significantly improving training efficiency. Moreover, we introduce a dual-branch diffusion transformer with decoupled timestep condition to separate the current noise state and the denoising target to enhance sampling quality. Extensive experiments demonstrate that TEASR supports seamless any-step sampling and consistently outperforms state-of-the-art methods across multiple datasets. Code: https://github.com/MediaX-SJTU/TEASR.

ECCV 2026

Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents

Dayong Liu, Chao Xu, Weihong Chen, Suyu Zhang, Juncheng Wang, Jiankang Deng, Baigui Sun, Yang Liu

Multimodal Large Language Models (MLLMs) show promising results as decision-making engines for embodied agents operating in complex, physical environments. However, existing benchmarks often prioritize high-level planning or spatial reasoning, leaving the fine-grained action intelligence required for embodied physical interaction underexplored. To address this gap, we introduce CFG-Bench, a new benchmark designed to systematically evaluate this crucial capability. CFG-Bench consists of 1,368 curated videos paired with 19,562 question-answer pairs spanning three evaluation paradigms targeting four cognitive abilities: 1) Physical Interaction, 2) Temporal-Causal Relation, 3) Intentional Understanding, and 4) Evaluative Judgment. Together, these dimensions provide a systematic framework for assessing a model’s ability to translate visual observations into actionable knowledge, moving beyond mere surface-level recognition. Our comprehensive evaluation on CFG-Bench reveals that leading MLLMs struggle to produce detailed instructions for physical interactions and exhibit profound limitations in the higher-order reasoning of intention and evaluation. Moreover, supervised fine-tuning (SFT) on our data demonstrates that teaching an MLLMs to articulate fine-grained actions directly translates to significant performance gains on established embodied benchmarks. Our analysis highlights these limitations and offers insights for developing more capable and grounded embodied agents. Project page: https://cfg-bench.github.io/

ECCV 2026

ObjectForesight: Predicting 3D Object Trajectories from Human Videos

Rustin Soraki, Homanga Bharadhwaj, Ali Farhadi, Roozbeh Mottaghi

Humans can effortlessly anticipate how objects might moveor change through interaction—imagining a cup being lifted, a knifeslicing, or a lid being closed. We aim to endow computational systemswith a similar ability to predict plausible future object motions directlyfrom passive visual observation. We introduce ObjectForesight, a 3Dobject-centric dynamics model that predicts future 6-DoF poses andtrajectories of rigid objects from short egocentric video sequences. Un-like conventional world/dynamics models that operate in pixel or latentspace, ObjectForesight represents the world explicitly in 3D at the objectlevel, enabling geometrically grounded and temporally coherent predic-tions that capture object affordances and trajectories. To train such amodel at scale, we leverage recent advances in segmentation, mesh re-construction, and 3D pose estimation to curate a dataset of 2+ millionshort clips with pseudo-ground-truth 3D object trajectories. Through ex-tensive experiments, we show that ObjectForesight achieves significantgains in accuracy, geometric consistency, and generalization to unseenobjects and scenes—establishing a scalable framework for learning phys-ically grounded, object-centric dynamics models directly from observa-tion. Project page: https://objectforesight.github.io

ECCV 2026

∂DIBR: Differentiable Depth Image-based Rendering for Fast Novel View Synthesis

Armand Losfeld, Sarah Dury, Gauthier Lafruit, Mehrdad Teratani, Daniele Bonatto

Modern Novel View Synthesis (NVS), such as 3D Gaussian Splatting (3DGS), introduces scene representations based on new parametrized primitives. These primitives are optimized through inverserendering optimization from color gradients, possibly with a depth prior. However, they fail to preserve geometric consistency and connectivity of the scene during the training phase. Furthermore, standard computer graphics (CG) pipelines do not easily handle these new primitives, thereby limiting rendering performance and integration into consumer applications. We propose a differentiable framework for Depth Image-Based Rendering (DIBR) using triangle primitives. Our method integrates seamlessly into traditional CG pipelines while remaining fully optimizable within modern NVS frameworks. Moreover, our primitives ensure geometric consistency and connectivity. Colors and depths are jointly optimized following a coarse-to-fine strategy. We compare our renderer to 3DGS, 2DGS, and Triangle Splatting across two forward-facing datasets. Although our approach yields slightly lower NVS quality (up to −3 dB), it achieves a 2× speed-up in rendering time (277 FPS) on a consumer-grade GPU, demonstrating its suitability for real-time or resource-constrained applications. Code: github.com/LISA-VR/ddibr.

ECCV 2026

DynEval: Holistic Evaluations of T2I Generative Models in the Wild

Shyam Marjit, Dheeraj Baiju, Anuj Shikarkhane, Akhil Sakthieswaran, Sayak Paul, Anirban Chakraborty

Recent advances in text-to-image (T2I) generation have led to models capable of producing highly realistic images. Yet, reliably evaluating their outputs remains challenging, especially at scale. Existing automatic evaluators, often relying on a static prompt set, struggle to capture subtle failure modes such as partial prompt misalignment, compositional errors or visually plausible but semantically incorrect generations. In this work, we introduce DynEval, a Dynamic Evaluation framework designed to jointly assess text-to-image alignment and image quality of T2I models. To support scalable training beyond limited humanannotated data, we construct two large datasets. First, we build GenDB, a collection of 500K prompt-image pairs generated from human-written prompts drawn from DiffusionDB using a tiered prompt-model generation strategy. Second, building upon GenDB, we construct DynEvalInstruct, a 250K instruction dataset comprising prompt-image-response triplets distilled from a structured evaluation pipeline that decomposes evaluation into text-image alignment and visual quality reasoning. Using this dataset, we perform full fine-tuning of a compact evaluator through a curriculum learning strategy to effectively distill the superior evaluation capabilities of a larger teacher vision-language model, resulting in DynEval-2B and DynEval-4B. In extensive comparisons against existing evaluators across 11 benchmarks, our evaluator achieves a higher overall correlation with human judgments. Furthermore, it provides finegrained analysis of the capabilities and failure modes of 36 T2I models across 42 subcategories and 9 semantic dimensions.

ECCV 2026

Setting the Stage: Text-Driven Scene-Consistent Image Generation

Cong Xie, Che Wang, Yan Zhang, Ruiqi Yu, Han Zou, Zheng Pan, Zhenpeng Zhan

We focus on the foundational task of Scene Staging: given a reference scene image and a text condition specifying an actor category to be generated in the scene and its spatial relation to the scene, the goal is to synthesize an output image that preserves the same scene identity as the reference image while correctly generating the actor according to the spatial relation described in the text. Existing methods struggle with this task, largely due to the scarcity of high-quality paired data and unconstrained generation objectives. To overcome the data bottleneck, we propose a novel data construction pipeline that combines real-world photographs, entity removal, and image-to-video diffusion models to generate training pairs with diverse scenes, viewpoints and correct entity-scene relationships. Furthermore, we introduce a novel correspondence-guided attention loss that leverages cross-view cues to enforce spatial alignment with the reference scene. Experiments on our scene-consistent benchmark show that our approach achieves better scene alignment and text–image alignment than state-of-the-art baselines, according to both automatic metrics and human preference studies. Our method generates images with diverse viewpoints and compositions while faithfully following the textual instructions and preserving the reference scene identity.

ECCV 2026

UniStitch: Unifying Semantic and Geometric Features for Image Stitching

Yuan Mei, Lang Nie, Kang Liao, Yunqiu Xu, Chunyu Lin, Bin Xiao

Traditional image stitching methods estimate warps from hand-crafted geometric features, whereas recent learning-based solutions leverage semantic features from neural networks instead. These two lines of research have largely diverged along separate evolution, with virtually no meaningful convergence to date. In this paper, we take a pioneering step to bridge this gap by unifying semantic and geometric features with UniStitch, a unified image stitching framework from multimodal features. To align discrete geometric features (i.e., keypoint) with continuous semantic feature maps, we present a Neural Point Transformer (NPT) module, which transforms unordered, sparse 1D geometric keypoints into ordered, dense 2D semantic maps. Then, to integrate the advantages of both representations, an Adaptive Mixture of Experts (AMoE) module is designed to fuse geometric and semantic representations. It dynamically shifts focus toward more reliable features during the fusion process, allowing the model to handle complex scenes, especially when either modality might be compromised. The fused representation can be adopted into common deep stitching pipelines, delivering significant performance gains over any single feature. Experiments show that UniStitch outperforms existing state-of-the-art methods with a large margin, paving the way for a unified paradigm between traditional and learning-based image stitching. Our project page is available at https://mmelodyy.github.io/projects/unistitch/.

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