GenAI4ELab/papercli-papers huggingface dataset:
Detecting Compressed AI-Generated Images via Phase Spectrum Robustness
Authors: Kai Li; Wenqi Ren; Wei Wang; Xiaochun Cao
Venue: CVPR 2026 (main)

Abstract: This paper aims to present a robust AI-generated image detection framework designed to address performance degradation caused by image compression in online social networks. The key challenges are twofold: 1) compression destroys fragile artifacts that are crucial to existing methods, and 2) it introduces new compression artifacts that interfere with detection. Existing methods typically enhance the compression robustness by collecting original-compression pairs and compression labels. However, the collection and annotation process is highly resource-intensive. To address these issues, we propose a Compression-Robust Phase-Harmonized Transformer, motivated by the observation that phase spectrum remains stable under compression. The framework consists of a phase-harmonized cross-modal interaction module that leverages phase spectrum information for feature fusion, enhancing compression robustness, and a multi-domain modulation adapter that further refines fused features while enabling parameter-efficient fine-tuning. In particular, the framework operates without requiring compression-original data pairs and compression labels. When limited compression labels are available, we introduce a difficulty-aware consistency loss to maximize their utility by prioritizing hard compressed samples during training, further boosting robustness. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches, exhibiting superior robustness against image compression.
VLM-Guided Group Preference Alignment for Diffusion-based Human Mesh Recovery
Authors: Wenhao Shen; Hao Wang; Wanqi Yin; Fayao Liu; Xulei Yang; Chao Liang; Zhongang Cai; Guosheng Lin
Venue: CVPR 2026 (main)

Abstract: Human mesh recovery (HMR) from a single RGB image is inherently ambiguous, as multiple 3D poses can correspond to the same 2D observation. Recent diffusion-based methods tackle this by generating various hypotheses, but often sacrifice accuracy. They yield predictions that are either physically implausible or drift from the input image, especially under occlusion or in cluttered, in-the-wild scenes. To address this, we introduce a dual-memory augmented HMR critique agent with self-reflection to produce context-aware quality scores for predicted meshes. These scores distill fine-grained cues about 3D human motion structure, physical feasibility, and alignment with the input image. We use these scores to build a group-wise HMR preference dataset. Leveraging this dataset, we propose a group preference alignment framework for finetuning diffusion-based HMR models. This process injects the rich preference signals into the model, guiding it to generate more physically plausible and image-consistent human meshes. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art approaches.
Tunable Soft Equivariance with Guarantees
Authors: Md Ashiqur Rahman; Lim Jun Hao; Jeremiah Jiang; Teck-Yian Lim; Raymond A. Yeh
Venue: CVPR 2026 (main)

Abstract: Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling the degree of equivariance is therefore desirable. We propose a general framework for constructing soft equivariant models by projecting the model weights into a designed subspace. The method applies to any pre-trained architecture and provides theoretical bounds on the induced equivariance error. Empirically, we demonstrate the effectiveness of our method on multiple pre-trained backbones, including ViT and ResNet, across image classification, semantic segmentation, and human-trajectory prediction tasks. Notably, our approach improves the performance while simultaneously reducing equivariance error on the competitive ImageNet benchmark.
PhysIR-Splat: Physically Consistent Thermal Infrared Radiative Transfer in 3D Gaussian Splatting
Authors: Jingyuan Gao; Yumeng Hu; Fei Gao; Mingjin Zhang
Venue: CVPR 2026 (main)

Abstract: Thermal infrared (TIR) 3D reconstruction provides geometry that is intrinsically coupled to the temperature field, even in low-light, nighttime, and smoke-obscured environments. TIR imaging measures self-emitted thermal radiation driven by object temperature and is largely independent of external illumination; therefore, simply carrying over visible-spectrum assumptions to TIR-based 3D reconstruction and novel view synthesis (NVS) often results in floating artifacts and blurred edges. In addition, radiometric inconsistency and low contrast in TIR weaken structure-from-motion (SfM) initialization, which in turn hinders subsequent 3D Gaussian Splatting (3DGS) optimization. We present PhysIR-Splat, a 3DGS framework that follows infrared radiative transfer: we explicitly model temperature, emissivity, and environmental irradiance on Gaussian primitives and, during rendering, jointly account for thermal emission, the reflected component, and atmospheric transmittance to produce physically consistent thermal synthesis. We also introduce VGGT-IR, a Transformer-based feed-forward initializer that takes TIR input with optional RGB and directly regresses camera poses and initial geometry, providing a modality-aligned and stable starting point for PhysIR-Splat. Extensive experiments demonstrate that our method significantly surpasses existing approaches in thermal reconstruction quality and cross-view consistency, effectively suppressing floating artifacts and enhancing boundary sharpness.  The source code will be publicly available at : https://github.com/JingyuanGao0919/physir-splat.
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
Authors: Ray Zhang; Marcus Greiff; Thomas Lew; John Subosits
Venue: CVPR 2026 (main)

Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings. The method represents point clouds as continuous functions with point-wise anisotropic kernels that encode local geometry. This formulation improves alignment along surface normals while relaxing alignment along tangential directions. To solve the resulting registration problem, we propose a second-order on-manifold optimization scheme with approximate Riemannian Hessians, achieving a speedup of up to 10x over the first-order solvers used in prior correspondence-free RKHS-based methods. We demonstrate improved frame-to-frame LiDAR and RGB-D tracking accuracy across diverse indoor and outdoor datasets. On a LiDAR tracking registration task in the driving domain, we achieve a reduction of >55% in both translational and rotational drift in challenging feature-sparse environments. On object registration benchmarks, we show improved robustness over ICP-based methods and further gains when refining global initialization, particularly under moderate misalignment.
MultiAnimate: Pose-Guided Image Animation Made Extensible
Authors: Yingcheng Hu; Haowen Gong; Chuanguang Yang; Zhulin An; Yongjun Xu; Songhua Liu
Venue: CVPR 2026 (main)

Abstract: Pose-guided human image animation aims to synthesize realistic videos of a reference character driven by a sequence of poses. While diffusion-based methods have achieved remarkable success, most existing approaches are limited to single-character animation. We observe that naively extending these methods to multi-character scenarios often leads to identity confusion and implausible occlusions between characters. To address these challenges, in this paper, we propose an extensible multi-character image animation framework built upon modern Diffusion Transformers (DiTs) for video generation. At its core, our framework introduces two novel components--Identifier Assigner and Identifier Adapter--which collaboratively capture per-person positional cues and inter-person spatial relationships. This mask-driven scheme, along with a scalable training strategy, not only enhances flexibility but also enables generalization to scenarios with more characters than those seen during training. Remarkably, trained on only a two-character dataset, our model generalizes to multi-character animation while maintaining compatibility with single-character cases. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in multi-character image animation, surpassing existing diffusion-based baselines. Codes will be released.
Exposing and Evaluating Hallucinations for GUI Grounding
Authors: Zicheng Zhang; Hongyi Jing; Rui Lv; Shuo Fang; Shiai Zhu; Junying Wang; Chunyi Li; Xiaohong Liu; Chenguang Ma; Guangtao Zhai
Venue: CVPR 2026 (main)

Abstract: Existing GUI benchmarks primarily focus on evaluating models' comprehensive capabilities but largely overlook hallucination phenomena in grounding tasks, which are crucial to the reliability of GUI understanding. In this work, we expose two major types of hallucinations in GUI grounding: 1) Confusion Hallucination, where distractor elements are mistakenly selected, and 2) Fabricated Hallucination, where nonexistent elements are hallucinated with plausible coordinates. To systematically investigate their origins, we introduce GUI-HalluBench, a benchmark comprising two complementary subsets: a parsing subset for assessing structural representation of GUI elements and a hallucination subset for measuring grounding robustness under challenging conditions. This design allows us to associate hallucination patterns with deficiencies in prerequisite abilities: parsing errors are closely tied to both fabricated and confusion hallucinations. Experiments on state-of-the-art models confirm these connections, offering new insights into the root causes of hallucinations and guiding the development of more reliable GUI understanding tools.
Beyond Sequential Tools: A Unified VLM Agent System for Photographic Post-Processing via Dynamic Multi-Expert Fusion
Authors: Honglin Xiong; Chenjie Zhu; Jianbiao Ding; Zixuan Ni; Wei Li; Zhenpeng Mi; Qian Wang
Venue: CVPR 2026 (main)

Abstract: Real-world image restoration is challenged by complex, coupled degradations. Existing "all-in-one" models often lack generalization, while agentic systems suffer from inefficient sequential tool invocation. We propose a VLM-guided one-shot framework for universal photographic post-processing. Our system employs a Vision-Language Model (VLM) as an orchestrator to perform nuanced intent understanding and degradation analysis, dynamically allocating weights to a suite of specialized expert LoRA modules. To ensure superior composability, these experts adapt only Key (K) and Value (V) matrices and are simultaneously merged into a pretrained diffusion backbone for synergistic, single-pass restoration. Furthermore, we introduce a lightweight branch trained via Direct Preference Optimization (DPO) to ensure perceptually optimal weight allocation. Our method achieves state-of-the-art performance across diverse synthetic and real-world datasets. Crucially, it demonstrates remarkable zero-shot generalization on authentic real-world data without additional fine-tuning.
ReAttnCLIP: Training-Free Open-Vocabulary Remote Sensing Image Segmentation via Re-defined Attention in CLIP
Authors: Xin Niu; Manqi Zhao; Dongsheng Jiang; Yingying Wu; Bing Su
Venue: CVPR 2026 (main)

Abstract: Remote sensing image segmentation is critical for a range of applications, including natural disaster monitoring and precision agriculture. Open-vocabulary segmentation enhances flexibility by removing fixed category constraints, enabling more fine-grained and adaptive scene understanding. Unlike CLIP's original pretraining objective, which emphasizes global image-text alignment, segmentation tasks require accurate and discriminative patch-level representations to support precise pixel-wise predictions. As a result, the quality of attention maps--particularly those generated in the final transformer layers--plays a pivotal role in guiding inter-region interactions. However, current methods generate suboptimal representations when capturing the complex spatial hierarchies in remote sensing. We address this gap by optimizing CLIP's 197x197 attention matrix through three key modifications: (1) substituting the 196x196 patch-to-patch submatrix with intermediate-layer feature similarities to preserve spatial structures; (2) prioritizing intermediate-layer attention for global-to-local (class-to-patch) token alignment to reduce classification interference; (3) disabling the \texttt [CLS]  token's self-attention to mitigate bias. Extensive experiments on eight remote sensing benchmarks and two building/road extraction datasets demonstrate that our method achieves state-of-the-art performance among existing training-free approaches.
LIBERO-Plus: A Progressive Robustness Benchmark for Visual-Language-Action Models
Authors: Senyu Fei; Siyin Wang; Junhao Shi; Zihao Dai; Jikun Cai; Pengfang Qian; Li Ji; Xinzhe He; Shiduo Zhang; Zhaoye Fei; Jinlan Fu; Jingjing Gong; Xipeng Qiu
Venue: CVPR 2026 (main)

Abstract: Visual-Language-Action (VLA) models report impressive success rates exceeding 95% on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. Current simulation-based robustness evaluations suffer from narrow perturbation coverage, manual design constraints, and coarse-grained analysis that fails to reveal when and how models fail. To address this gap, we propose LIBERO-Plus, a comprehensive, automatic, and fine-grained evaluation framework with controlled perturbations across seven dimensions: object layouts, camera viewpoints, robot initial states, language instructions, lighting conditions, background textures, and sensor noise. Our systematic analysis of ten state-of-the-art models reveals consistent brittleness beneath apparent competence, with performance dropping from 95% to below 30% under modest perturbations. Our findings challenge the assumption that high benchmark scores equate to true competency and highlight the need for evaluation practices that assess reliability under realistic variation.
MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts
Authors: Zilong Huang; Jun He; Xiaobin Huang; Ziyi Xiong; Yang Luo; Junyan Ye; Weijia Li; Yiping Chen; Ting Han
Venue: CVPR 2026 (main)

Abstract: Generating realistic 3D cities is fundamental to world models, virtual reality, and game development, where an ideal urban scene must satisfy both stylistic diversity, fine-grained, and controllability. However, existing methods struggle to balance the creative flexibility offered by text-based generation with the object-level editability enabled by explicit structural representations. We introduce MajutsuCity, a natural language-driven and aesthetically adaptive framework for synthesizing structurally consistent and stylistically diverse 3D urban scenes. MajutsuCity represents a city as a composition of controllable layouts, assets, and materials, and operates through a four-stage pipeline. To extend controllability beyond initial generation, we further integrate MajutsuAgent, an interactive language-grounded editing agent that supports five object-level operations. To support photorealistic and customizable scene synthesis, we also construct MajutsuDataset, a high-quality multimodal dataset containing 2D semantic layouts and height maps, diverse 3D building assets, and curated PBR materials and skyboxes, each accompanied by detailed annotations. Meanwhile, we develop a practical set of evaluation metrics, covering key dimensions such as structural consistency, scene complexity, material fidelity, and lighting atmosphere. Extensive experiments demonstrate MajutsuCity reduces layout FID by 83.7% compared with CityDreamer and by 20.1% over CityCraft. Our method ranks first across all AQS and RDR scores, outperforming existing methods by a clear margin. These results confirm MajutsuCity as a new state-of-the-art in geometric fidelity, stylistic adaptability, and semantic controllability for 3D city generation. We expect our framework can inspire new avenues of research in 3D city generation. Our project page: https://longhz140516.github.io/MajutsuCity/
AnyLift: Scaling Motion Reconstruction from Internet Videos via 2D Diffusion
Authors: Hongjie Li; Heng Yu; Jiaman Li; Hong-Xing Yu; Ehsan Adeli; C. Karen Liu; Jiajun Wu
Venue: CVPR 2026 (main)

Abstract: Reconstructing 3D human motion and human-object interactions (HOI) from Internet videos is a fundamental step toward building large-scale datasets of human behavior. Existing methods struggle to recover globally consistent 3D motion under dynamic cameras, especially for motion types underrepresented in current motion-capture datasets, and face additional difficulty recovering coherent human-object interactions in 3D. We introduce a two-stage framework leveraging 2D diffusion that reconstructs 3D human motion and HOI from Internet videos. In the first stage, we synthesize multi-view 2D motion data for each domain, leveraging 2D keypoints extracted from Internet videos to incorporate human motions that rarely appear in existing MoCap datasets. In the second stage, a camera-conditioned multi-view 2D motion diffusion model is trained on the domain-specific synthetic data to recover 3D human motion and 3D HOI in the world space. We demonstrate the effectiveness of our method on Internet videos featuring challenging motions such as gymnastics, as well as in-the-wild HOI videos, and show that it outperforms prior work in producing realistic human motion and human-object interaction.
Accelerating Diffusion via Hybrid Data-Pipeline Parallelism Based on Conditional Guidance Scheduling
Authors: Euisoo Jung; Byunghyun Kim; Hyunjin Kim; Seonghye Cho; Jae-Gil Lee
Venue: CVPR 2026 (main)

Abstract: Diffusion models have achieved remarkable progress in high-fidelity image, video, and audio generation, yet inference remains computationally expensive. Nevertheless, current diffusion acceleration methods based on distributed parallelism suffer from noticeable generation artifacts and fail to achieve substantial acceleration proportional to the number of GPUs. Therefore, we propose a hybrid parallelism framework that combines a novel data parallel strategy, condition-based partitioning, with an optimal pipeline scheduling method, adaptive parallelism switching, to reduce generation latency and achieve high generation quality in conditional diffusion models. The key ideas are to (i) leverage the conditional and unconditional denoising paths as a new data-partitioning perspective and (ii) adaptively enable optimal pipeline parallelism according to the denoising discrepancy between these two paths. Our framework achieves 2.31xand 2.07xlatency reductions on SDXL and SD3, respectively, using two NVIDIA RTX 3090 GPUs, while preserving image quality. This result confirms the generality of our approach across U-Net-based diffusion models and DiT-based flow-matching architectures. Our approach also outperforms existing methods in acceleration under high-resolution synthesis settings. Code is available at https://github.com/kaist-dmlab/Hybridiff.
OpenDance: Multimodal Controllable 3D Dance Generation with Large-scale Internet Data
Authors: Jinlu Zhang; Zixi Kang; Libin Liu; Jianlong Chang; Qi Tian; Feng Gao; Yizhou Wang
Venue: CVPR 2026 (main)

Abstract: Music-driven 3D dance generation offers significant creative potential, yet practical applications demand versatile and multimodal control. Given the highly dynamic and complex human motion covering various styles and genres, dance generation requires satisfying diverse conditions beyond just music (e.g., spatial trajectories, keyframe gestures, or style descriptions). However, the absence of a large-scale and richly annotated dataset severely hinders progress. In this paper, we build OpenDanceSet, an extensive human dance dataset comprising over 100 hours across 14 genres and 147 subjects. Each sample has rich annotations to facilitate robust cross-modal learning: 3D motion, paired music, 2D keypoints, trajectories, and expert-annotated text descriptions. Furthermore, we propose OpenDanceNet, a unified masked modeling framework for controllable dance generation, including a disentangled auto-encoder and a multimodal joint-prediction Transformer. OpenDanceNet supports generation conditioned on music and arbitrary combinations of text, keypoints, or trajectories. Comprehensive experiments demonstrate that our work achieves high-fidelity synthesis with strong diversity and realistic physical contacts, while also offering flexible control over spatial and stylistic conditions.
UniVerse: Empower Unified Generation with Reasoning and Knowledge
Authors: Kaiyue Sun; Weiyang Jin; Chengqi Duan; Rongyao Fang; Xian Liu; Yuwei Niu; Chunwei Wang; Aoxue Li; Xihui Liu
Venue: CVPR 2026 (main)

Abstract: Current text-to-image (T2I) generation models often struggle with prompts that require complex reasoning or specialized knowledge, failing to accurately interpret implicit user intent. To bridge this gap, we introduce T2I-Reason, a large-scale dataset designed to empower text-to-image generation in unified multimodal models (UMMs) with reasoning and knowledge. The dataset contains 120k pairs of text triplet and image. The text triplet consists of (1) an implicit prompt, which requires reasoning or knowledge to decipher its underlying meaning; (2) a reasoning chain, which provides a step-by-step analysis to resolve the implicit prompt's meaning; and (3) an explicit prompt, a clear and straightforward visual description prepared for T2I generation. T2I-Reason is meticulously constructed: 65k samples are dedicated to reasoning, specifically targeting arithmetic reasoning, spatial-attribute relationship reasoning, deductive reasoning (cause to effect), and abductive reasoning (effect to cause). While 55k samples necessitate specialized knowledge, which covers multiple disciplines, spatial-temporal concepts, and entity knowledge. To validate the effectiveness of our dataset, we train a unified multimodal model, Bagel, on our dataset. Results across multiple benchmarks that evaluate the reasoning capabilities of T2I generation demonstrate that our model achieves significant and consistent improvements on both composition and reasoning, confirming that explicit training on intermediate reasoning chains is a pivotal step towards more intelligent unified generative models.
EcoAlign: An Economically Rational Framework for Efficient LVLM Alignment
Authors: Ruoxi Cheng; Hao-Xuan Ma; Teng Ma; Hongyi Zhang
Venue: CVPR 2026 (main)

Abstract: Large Vision-Language Models (LVLMs) exhibit powerful reasoning capabilities but suffer sophisticated jailbreak vulnerabilities. Fundamentally, aligning LVLMs is not just a safety challenge but a problem of economic efficiency. Current alignment methods struggle with the trade-off between safety, utility, and operational costs. Critically, a focus solely on final outputs (process-blindness) wastes significant computational budget on unsafe deliberation. This flaw allows harmful reasoning to be disguised with benign justifications, thereby circumventing simple additive safety scores.To address this, we propose EcoAlign, an inference-time framework that reframes alignment as an economically rational search by treating the LVLM as a boundedly rational agent. EcoAlign incrementally expands a thought graph and scores actions using a forward-looking function (analogous to net present value) that dynamically weighs expected safety, utility, and cost against the remaining budget. To prevent deception, path safety is enforced via the weakest-link principle. Extensive experiments across 3 closed-source and 2 open-source models on 6 datasets show that EcoAlign matches or surpasses state-of-the-art safety and utility at a lower computational cost, thereby offering a principled, economical pathway to robust LVLM alignment.