GenAI4ELab/papercli-papers huggingface dataset:
Test-Time Multi-Prompt Adaptation for Open-Vocabulary Remote Sensing Image Segmentation
Authors: Ting Yang; Qilong Wang; Qibin Hou; Qinghua Hu
Venue: CVPR 2026 (main)

Abstract: The rise of vision-language models (VLMs) has driven the initial exploration of open-vocabulary remote sensing image semantic segmentation (OVRSIS), enabling recognition of unseen categories in complex Earth observation scenes. However, existing methods primarily focus on enhancing visual representations of domain-specific remote sensing images, while overlooking the effect of textual information. In this paper, we argue that there exists a crucial issue of textual ambiguity in OVRSIS task, limiting final segmentation performance. Therefore, we propose a plug-and-play yet effective Test-time Multi-Prompt Adaptation (TMPA) method to mitigate textual ambiguity in OVRSIS. Specifically, TMPA first generates diverse, context-aware descriptions for each category instead of the naive class name by executing a large language model with a task-driven prompt, which can effectively avoid some textual ambiguity, i.e., background class has different meanings in various tasks. Furthermore, TMPA develops a visual-guided test-time adaptation strategy for the generated multi-prompts, which adaptively refines the prompt representations of each category with high-confidence visual features for the uncertain predictions with high entropy, making TMPA better applicable to different scenarios. Particularly, a pixel-level loss with entropy minimization is proposed to optimize the text prompt with a bias during inference, where prompt bias is constructed based on a weighted combination of high-confidence visual features. Our TMPA can be flexibly integrated into existing methods for boosting their performance. Extensive experiments are conducted on 17 remote sensing datasets, and the results show our TMPA can significantly improve its counterparts, while achieving state-of-the-art performance.
The Midas Touch for Metric Depth
Authors: Yu Ma; Zizhan Guo; Zuyi Xiong; Haoran Zhang; Yi Feng; Hongbo Zhao; Hanli Wang; Rui Fan
Venue: CVPR 2026 (main)

Abstract: Recent advances have markedly improved the cross-scene generalization of relative depth estimation, yet its practical applicability remains limited by the absence of metric scale, local inconsistencies, and low computational efficiency. To address these issues, we present Midas Touch for Depth (MTD), a mathematically interpretable approach that converts relative depth into metric depth using only extremely sparse 3D data. To eliminate local scale inconsistencies, it applies a segment-wise recovery strategy via sparse graph optimization, followed by a pixel-wise refinement strategy using a discontinuity-aware geodesic cost. MTD exhibits strong generalization and achieves substantial accuracy improvements over previous depth completion and depth estimation methods. Moreover, its lightweight, plug-and-play design facilitates deployment and integration on diverse downstream 3D tasks.
Wavelet-Driven 3D Anomaly Detection under Pose-Agnostic and Sparse-View
Authors: Mingwen Shao; Qiao Zhang; Xinyuan Chen; Xiang Lv; Lingzhuang Meng; Chang Liu; Qinglin Zhan; Ling Jian
Venue: CVPR 2026 (main)

Abstract: Pose-agnostic anomaly detection (PAD) achieves strong performance in localizing anomalies from arbitrary viewpoints when trained on densely sampled normal data. However, under sparse-view conditions, existing methods face two key challenges: (1) sparse observations lead to overfitting and geometric detail loss in 3D reconstruction; (2) limited visual cues lead to inaccurate pose estimation, compromising the reliability of subsequent anomaly localization. To address these challenges, we propose Wave-Pose3D, a wavelet-driven 3D anomaly detection framework tailored for PAD under sparse-view conditions. First, we design a structure-aware and wavelet-optimized Gaussian modeling strategy that dynamically filters unreliable regions via structural priors to mitigate overfitting and leverages high-frequency supervision to restore fine-grained geometric details. Second, to improve pose estimation under sparse views, we develop a wavelet-based pose estimator that integrates low-frequency structural cues and high-frequency details to enhance both initialization and refinement accuracy. Finally, we introduce a wavelet difference-aware anomaly detector that computes frequency-domain anomaly scores, improving localization robustness against pose and geometric variations. By integrating these strategies, Wave-Pose3D achieves robust and accurate anomaly localization under sparse views. Extensive experiments validate that the proposed approach achieves state-of-the-art performance under 10% and 20% sparse-view configurations.
OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens
Authors: Yiying Yang; Wei Cheng; Sijin Chen; Honghao Fu; Xianfang Zeng; Yujun Cai; Gang Yu; Xingjun Ma
Venue: CVPR 2026 (main)

Abstract: OmniLottie is a versatile framework that generates high-quality vector animations from multi-modal instructions, including interleaved texts, images, and videos. To fully parameterize vector animations for flexible motion and visual content control, we seek help from the Lottie representation, which encodes both shapes and animated behaviors in a single JSON file. Building upon a pretrained vision-language model (VLM), OmniLottie produces vivid, semantically aligned vector animations that adhere closely to multi-modal conditions. To avoid the complexity and irregularity of raw JSON structures, we introduce a dedicated Lottie tokenizer that transforms Lottie files into structured sequences of function calls representing shapes, animation commands, and their parameters. This design enables the model to directly learn the underlying shape and animation priors from data, substantially improving generation stability and controllability. To further advance research in vector animation generation, we curate MMLottie-2M, a large-scale dataset of professionally designed vector animations paired with textual and visual annotations. Leveraging the well-designed tokenizer and our newly established dataset, OmniLottie demonstrates strong multi-modal conditional generation capabilities using a simple next-token prediction objective. For qualitative results, please refer to the generated animations rendered through standard Lottie players on the supplementary website.
GeneVAR: Causal MeanFlow for Autoregressive Gene-to-WSI Tile Synthesis
Authors: Jianwei Zhao; Fan Yang; Xin Li; Qiang Zhai; Ao Luo; Ziqi Ren; Zhicheng Jiao; Hong Cheng
Venue: CVPR 2026 (main)

Abstract: Understanding how transcriptomic programs shape tissue morphology remains a central challenge in computational pathology. Gene-to-WSI tile synthesis offers a principled generative framework to translate molecular profiles into histological images. However, most existing methods compress RNA-Seq into a single global embedding injected once at initialization, an oversimplified design that weakens transcriptomic signals and induces spurious, non-biological associations. We present GeneVAR, an Autoregressive Gene-to-WSI model that reformulates synthesis as an iterative, coarse-to-fine generative process. At its core is a novel Causal MeanFlow module, which leverages counterfactual interventions to suppress non-biological variations (e.g., staining or contrast artifacts) and enforce an artifact-invariant generation process. Concurrently, it reinforces transcriptome-informed guidance across multiple stages, preserving biological fidelity throughout the generative trajectory. Combined with a b-VAE for compact gene embeddings and a multi-scale vector quantizer for discrete morphology representation, GeneVAR generates H&E-stained WSI tiles that are both visually realistic and transcriptomically faithful. Extensive experiments across five TCGA cancer benchmarks demonstrate consistent state-of-the-art performance, surpassing prior methods in both generative fidelity and downstream classification accuracy.
Narrative Weaver: Towards Controllable Long-Range Visual Consistency with Multi-Modal Conditioning
Authors: Zhengjian Yao; Yongzhi Li; Xinyuan Gao; Quan Chen; Peng Jiang; Yanye Lu
Venue: CVPR 2026 (main)

Abstract: We present Narrative Weaver, a novel framework that addresses a fundamental challenge in generative AI: achieving controllable, long-range, and consistent visual content generation. While existing models excel at generating high-fidelity short-form visual content, they struggle to maintain narrative coherence and visual consistency across extended sequences--a critical limitation for real-world applications such as filmmaking and e-commerce advertising.Narrative Weaver introduces the first holistic solution that seamlessly integrates three essential capabilities: fine-grained control, automatic narrative planning, and long-range coherence. Our architecture combines a Multimodal Large Language Model (MLLM) for high-level narrative planning with a novel fine-grained control module featuring a dynamic Memory Bank that prevents visual drift.To enable practical deployment, we develop a progressive, multi-stage training strategy that efficiently leverages existing pre-trained models, achieving state-of-the-art performance even with limited training data. Recognizing the absence of suitable evaluation benchmarks, we construct and release the E-commerce Advertising Video Storyboard Dataset (EAVSD)--the first comprehensive dataset for this task, containing over 330K high-quality images with rich narrative annotations.Through extensive experiments across three distinct scenarios (controllable multi-scene generation, autonomous storytelling, and e-commerce advertising), we demonstrate our method's superiority while opening new possibilities for AI-driven content creation.
Bilevel Layer-Positioning LoRA for Real Image Dehazing
Authors: Yan Zhang; Long Ma; Yuxin Feng; Zhe Huang; Fan Zhou; Zhuo Su
Venue: CVPR 2026 (main)

Abstract: Learning-based real image dehazing methods have achieved notable progress, yet they still face adaptation challenges in diverse real haze scenes. These challenges mainly stem from the lack of effective unsupervised mechanisms for unlabeled data and the heavy cost of full model fine-tuning. To address these challenges, we propose the haze-to-clear text-directed loss that leverages CLIP's cross-modal capabilities to reformulate real image dehazing as a semantic alignment problem in latent space, thereby providing explicit unsupervised cross-modal guidance in the absence of reference images. Furthermore, we introduce the Bilevel Layer-positioning LoRA (BiLaLoRA) strategy, which learns both the LoRA parameters and automatically search the injection layers, enabling targeted adaptation of critical network layers. Extensive experiments demonstrate our superiority against state-of-the-art methods on multiple real-world dehazing benchmarks.
TrajTok: Learning Trajectory Tokens Enhances Video Understanding
Authors: Chenhao Zheng; Jieyu Zhang; Jianing Zhang; Weikai Huang; Ashutosh Kumar; Quan Kong; Oncel Tuzel; Chun-Liang Li; Ranjay Krishna
Venue: CVPR 2026 (main)

Abstract: Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video efficiency and scalability. While the recent trajectory-based tokenizers offer a promising solution by decoupling video duration from token count, they rely on complex, external segmentation and tracking pipelines that are slow and task-agnostic. We propose TrajTok, an end-to-end video tokenizer module that is fully integrated and co-trained with video models for a downstream objective, dynamically adapting its token granularity to semantic complexity, independent of video duration. TrajTok contains a unified segmenter that performs implicit clustering over pixels in both space and time to directly produce object trajectories in a single forward pass. By prioritizing downstream adaptability over pixel-perfect segmentation fidelity, TrajTok is lightweight, efficient, and yet empirically improves video understanding performance. With TrajTok, we implement a video CLIP model trained from scratch (TrajViT2). It achieves the best accuracy at scale across both classification and retrieval benchmarks, while maintaining efficiency comparable to the best token-merging methods. TrajTok also proves to be a versatile component beyond its role as a tokenizer. We show that it can be seamlessly integrated as either a probing head for pretrained visual features (TrajAdapter) or an alignment connector in vision-language models (TrajVLM) with especially strong performance in long-video reasoning.
3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding
Authors: Makanjuola Adekunmi Ogunleye; Eman Abdelrahman; Ismini Lourentzou
Venue: CVPR 2026 (main)

Abstract: Large Language Models are increasingly integrated as the cognitive core of 3D embodied agents to enable complex environmental reasoning. However, these agents tend to inherit the critical flaw of hallucination, often failing to ground their responses to their 3D view. While Visual Contrastive Decoding (VCD) is a powerful training-free method for mitigating hallucinations in 2D image-based models, it has not been adapted to the complex 3D embodied environment. In this paper, we embarked on the ambitious goal of being the very first to bridge this gap by introducing a VCD framework for 3D embodied agents. Our method operates at inference time by generating a "negative" 3D context, not by blurring an image, but by applying novel distortions directly to a 3D scene graph, such as swapping object category labels or noising positional coordinates. We evaluate our approach on standard evaluation benchmarks and find that it consistently outperforms existing models. For example, in the random category of 3D-POPE, our 3D-VCD method reduced Yes-rate from 99.9% to 75.1% while simultaneously increasing precision from 50.0% to 62.2%. These results demonstrate that our training-free approach effectively curbs hallucination, yielding 3D agents that are significantly more reliable and grounded.
SAT-RRG: LLM-Guided Self-Adaptive Training for Radiology Report Generation with Token-Level Push-Pull Optimization
Authors: Yunyi Liu; Yingshu Li; Tong Chen; Lingqiao Liu; Lei Wang; Luping Zhou
Venue: CVPR 2026 (main)

Abstract: Radiology report generators often produce fluent text yet miss crucial details, leading to local semantic conflicts or flipped findings that require stronger penalties. **Cross-entropy (CE) merely increases the probability of the ground-truth token y^* without directly suppressing the model's current wrong choice \hat y **, and treats all positions uniformly, so corrections are not prioritized. We introduce a **self-adaptive optimization framework** that dynamically adjusts token-level gradients based on semantic discrepancy cues derived from a frozen LLM referee. The LLM itself is not the contribution--it merely provides weak supervision to trigger the adaptive learning process. Within this framework, (i) semantic conflicts between the predicted and reference reports are **automatically localized** and tagged with "<e>...</e>" (used only during training), and (ii) **adaptive, stronger penalties** are applied within these sparse but critical spans. Updates follow a *push-pull* scheme: error spans are pushed down, while non-error tokens are reinforced. The update strength is governed by two complementary signals--*normalized entropy* (for uncertainty calibration) and *focal-style confidence* (for handling over- and under-confident predictions). On MIMIC-CXR and IU-Xray, our framework consistently improves both language metrics (BLEU-4, ROUGE-L, CIDEr) and clinical metrics (RadGraph F1, CheXbert), and remains robust to noisy or imperfect error tags.
MoRE: 3D Visual Geometry Reconstruction Meets Mixture-of-Experts
Authors: Jingnan Gao; Zhe Wang; Xianze Fang; Xingyu Ren; Zhuo Chen; Shengqi Liu; Yuhao Cheng; Jiangjing Lyu; Xiaokang Yang; Yichao Yan
Venue: CVPR 2026 (main)

Abstract: Recent advances in language and vision have demonstrated that scaling up model capacity consistently improves performance across diverse tasks.In 3D visual geometry reconstruction, large-scale training has likewise proven effective for learning versatile representations.However, further scaling of 3D models is challenging due to the complexity of geometric supervision and the diversity of 3D data. To overcome these limitations, we propose MoRE, a dense 3D visual foundation model based on a Mixture-of-Experts (MoE) architecture that dynamically routes features to task-specific experts, allowing them to specialize in complementary data aspects and enhance both scalability and adaptability.Aiming to improve robustness under real-world conditions, MoRE incorporates a confidence-based depth refinement module that stabilizes and refines geometric estimation.In addition, it integrates dense semantic features with globally aligned 3D backbone representations for high-fidelity surface normal prediction.MoRE is further optimized with tailored loss functions to ensure robust learning across diverse inputs and multiple geometric tasks.Extensive experiments demonstrate that MoRE achieves state-of-the-art performance across multiple benchmarks and supports effective downstream applications without extra computation.
UniVerse: A Unified Modulation Framework for Segmentation-Free, Disentangled Multi-Concept Personalization
Authors: Quynh Phung; Sandesh Ghimire; Minsi Hu; Chung-Chi Tsai; Jia-Bin Huang
Venue: CVPR 2026 (main)

Abstract: Personalized visual understanding has advanced significantly, yet existing approaches struggle to localize and extract specific concepts when input images contain multiple objects. Many prior methods rely heavily on segmentation-based supervision or exhibit poor compositional generalization, limiting their ability to accurately disentangle and manipulate individual concepts. In this work, we propose UniVerse, a Unified Modulation Framework for segmentation-free, disentangled multi-concept personalization in diffusion transformers. Our method allows for composable and decomposable concept extraction, enabling fine-grained localization and representation of target objects without explicit segmentation masks. UniVerse learns to decompose complex scenes into concept-specific representations and then compose them in a unified manner, enabling robust personalization across diverse visual contexts. Through extensive experiments on multiple benchmarks, we demonstrate that UniVerse significantly outperforms state-of-the-art baselines in both localization accuracy and visual fidelity. Qualitative and quantitative results show that our approach can precisely extract target concepts in cluttered scenes, paving the way for more flexible, interpretable, and personalized visual generation and understanding.
GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry
Authors: Xiankang He; Peile Lin; Ying Cui; Dongyan Guo; Chunhua Shen; Xiaoqin Zhang
Venue: CVPR 2026 (main)

Abstract: Motion segmentation in dynamic scenes is highly challenging, as conventional methods heavily rely on estimating camera poses and point correspondences from inherently noisy motion cues. Existing statistical inference or iterative optimization techniques that struggle to mitigate the cumulative errors in multi-stage pipelines often lead to limited performance or high computational cost. In contrast, we propose a fully learning-based approach that directly infers moving objects from latent feature representations via attention mechanisms, thus enabling end-to-end feed-forward motion segmentation. Our key insight is to bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion. Supported by recent advances in 4D scene geometry reconstruction (e.g., \pi^3), the proposed method leverages reliable camera poses and rich spatial-temporal priors, which ensure stable training and robust inference for the model. Extensive experiments demonstrate that by eliminating complex pre-processing and iterative refinement, our approach achieves state-of-the-art motion segmentation performance with high efficiency. The code is available at:https://github.com/zjutcvg/GeoMotion.
Generative Point Tracking and Forecasting
Authors: Xuanchen Lu; Ang Cao; Chao Feng; Andrew Owens
Venue: CVPR 2026 (main)

Abstract: Motion forecasting predicts where points will move in the future, while motion tracking predicts where they are in the present. Despite these similarities, existing approaches to the two problems are quite different. In this paper, we propose a unified model that can address both tasks. We train a causal, video-conditioned flow matching model to predict point positions. The resulting model can easily toggle between point tracking to forecasting by changing its visual signal. Despite our model's simplicity, we find that it outperforms prior work in point forecasting and obtains performance that is competitive with the state-of-the-art on the TAP-Vid benchmark.
Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking
Authors: Hongtao Yang; Bineng Zhong; Qihua Liang; Yaozong Zheng; Xiantao Hu; Yuanliang Xue; Shuxiang Song
Venue: CVPR 2026 (main)

Abstract: Given the real-time demands of UAV tracking, many methods simplify the backbone to reduce computation, but this often weakens feature representation and degrades performance in complex scenarios. To alleviate this issue, we propose EATrack, an efficient and asymmetric UAV tracking framework centered around a teacher-guided dual-branch distillation strategy that enhances the feature expressiveness of the lightweight student model. Specifically, EATrack investigates two complementary perspectives of knowledge transfer: a spatially focused feature-level distillation that compensates for weakened representations by guiding the student to learn strong target representations, and a prediction-level distillation that enhances spatial localization by learning the teacher's capability of accurate target localization. Furthermore, to enhance robustness against appearance variations, we introduce a fine-grained target-aware distillation strategy that selectively transfers the teacher's target modeling capacity to the student. A temporal adaptation module is incorporated at inference to enhance robustness over time. Experiments on five UAV benchmarks demonstrate that EATrack achieves a favorable balance between accuracy and speed, with EATrack-DeiT improving average success rate by 1.2% over the previous SOTA while running at 241.9 FPS on GPU.
DiffSoup: Direct Differentiable Rasterization of Triangle Soup for Extreme Radiance Field Simplification
Authors: Kenji Tojo; Bernd Bickel; Nobuyuki Umetani
Venue: CVPR 2026 (main)

Abstract: Radiance field reconstruction aims to recover high-quality 3D representations from multi-view RGB images. Recent advances, such as 3D Gaussian splatting, enable real-time rendering with high visual fidelity on sufficiently powerful graphics hardware. However, efficient online transmission and rendering across diverse platforms requires drastic model simplification, reducing the number of primitives by several orders of magnitude. We introduce DiffSoup, a radiance field representation that employs a soup (i.e., a highly unstructured set) of a small number of triangles with neural textures and binary opacity. We show that this binary opacity representation is directly differentiable via stochastic opacity masking, enabling stable training without a mollifier (i.e., smooth rasterization). DiffSoup can be rasterized using standard depth testing, enabling seamless integration into traditional graphics pipelines and interactive rendering on consumer-grade laptops and mobile devices.