GenAI4ELab/papercli-papers huggingface dataset:
ZeroIDIR: Zero-Reference Illumination Degradation Image Restoration with Perturbed Consistency Diffusion Models
Authors: Hai Jiang; Zhen Liu; Yinjie Lei; Songchen Han; Bing Zeng; Shuaicheng Liu
Venue: CVPR 2026 (main)

Abstract: In this paper, we propose a zero-reference diffusion-based framework, named ZeroIDIR, for illumination degradation image restoration, which decouples the restoration process into adaptive illumination correction and diffusion-based reconstruction while being trained solely on low-quality degraded images. Specifically, we design an adaptive gamma correction module that performs spatially varying exposure correction to generate illumination-corrected only representations to mitigate exposure bias and serve as reliable inputs for subsequent diffusion processes, where a histogram-guided illumination correction loss is introduced to regularize the corrected illumination distribution toward that of natural scenes. Subsequently, the illumination-corrected image is treated as an intermediate noisy state for the proposed perturbed consistency diffusion model to reconstruct details and suppress noise. Moreover, a perturbed diffusion consistency loss is proposed to constrain the forward diffusion trajectory of the final restored image to remain consistent with the perturbed state, thus improving restoration fidelity and stability in the absence of supervision. Extensive experiments on publicly available benchmarks show that the proposed method outperforms state-of-the-art unsupervised competitors and is comparable to supervised methods while being more generalizable to various scenes. Code is available at https://github.com/JianghaiSCU/ZeroIDIR.
HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images
Authors: Yichen Liu; Donghao Zhou; Jie Wang; Xin Gao; Guisheng Liu; Jiatong Li; Quanwei Zhang; Qiang Lyu; Lanqing Guo; Shilei Wen; Weiqiang Wang; Pheng-Ann Heng
Venue: CVPR 2026 (main)

Abstract: Human-product images, which showcase the integration of humans and products, play a vital role in advertising, e-commerce, and digital marketing. The essential challenge of generating such images lies in ensuring the high-fidelity preservation of product details. Among existing paradigms, reference-based inpainting offers a targeted solution by leveraging product reference images to guide the inpainting process. However, limitations remain in three key aspects: the lack of diverse large-scale training data, the struggle of current models to focus on product detail preservation, and the inability of coarse supervision for achieving precise guidance. To address these issues, we propose HiFi-Inpaint, a novel high-fidelity reference-based inpainting framework tailored for generating human-product images. HiFi-Inpaint introduces Shared Enhancement Attention (SEA) to refine fine-grained product features and Detail-Aware Loss (DAL) to enforce precise pixel-level supervision using high-frequency maps. Additionally, we construct a new dataset, HP-Image-40K, with samples curated from self-synthesis data and processed with automatic filtering. Experimental results show that HiFi-Inpaint achieves state-of-the-art performance, delivering detail-preserving human-product images.
3D-LATTE: Latent Space 3D Editing from Textual Instructions
Authors: Maria Parelli; Michael Oechsle; Michael Niemeyer; Federico Tombari; Andreas Geiger
Venue: CVPR 2026 (main)

Abstract: Despite the recent success of multi-view diffusion models for text/image-based 3D asset generation, instruction-based editing of 3D assets lacks surprisingly far behind the quality of generation models. The main reason is that recent approaches using 2D priors suffer from view-inconsistent editing signals. Going beyond 2D prior distillation methods and multi-view editing strategies, we propose a novel editing method that operates within the latent space of a native 3D diffusion model, allowing us to directly manipulate 3D geometry. We guide the edit synthesis by blending 3D attention maps from the generation with the source object. Coupled with geometry-aware regularization guidance, a spectral modulation strategy in the Fourier domain and a refinement step for 3D enhancement, our method outperforms previous 3D editing methods enabling high-fidelity and precise edits across a wide range of shapes and semantic manipulations. Our project webpage is https://mparelli.github.io/3d-latte.
NI-Tex: Non-isometric Image-based Garment Texture Generation
Authors: Hui Shan; Ming Li; Haitao Yang; Kai Zheng; Sizhe Zheng; Yanwei Fu; Xiangru Huang
Venue: CVPR 2026 (main)

Abstract: Existing industrial 3D garment meshes already cover most real-world clothing geometries, yet their texture diversity remains limited. To acquire more realistic textures, generative methods are often used to extract Physically-based Rendering (PBR) textures and materials from large collections of wild images and project them back onto garment meshes. However, most image-conditioned texture generation approaches require strict topological consistency between the input image and the input 3D mesh, or rely on accurate mesh deformation to match to the image poses, which significantly constrains the texture generation quality and flexibility.To address the challenging problem of non-isometric image-based garment texture generation, we construct 3D Garment Videos, a physically simulated, garment-centric dataset that provides consistent geometry and material supervision across diverse deformations, enabling robust cross-pose texture learning. We further employ Nano Banana for high-quality non-isometric image editing, achieving reliable cross-topology texture generation between non-isometric image-geometry pairs. Finally, we propose an iterative baking method via uncertainty-guided view selection and reweighting that fuses multi-view predictions into seamless, production-ready PBR textures. Through extensive experiments, we demonstrate that our feedforward dual-branch architecture generates versatile and spatially aligned PBR materials suitable for industry-level 3D garment design.
Active Intelligence in Video Avatars via Closed-loop World Modeling
Authors: Xuanhua He; Tianyu Yang; Ke Cao; Ruiqi Wu; Cheng Meng; Yong Zhang; Zhuoliang Kang; Xiaoming Wei; Qifeng Chen
Venue: CVPR 2026 (main)

Abstract: Current video avatar generation methods excel at identity preservation and motion alignment but lack genuine agency--they cannot autonomously pursue long-term goals through adaptive environmental interaction. We address this by introducing L-IVA (Long-horizon Interactive Visual Avatar), a task and benchmark for evaluating goal-directed planning in stochastic generative environments, and ORCA (Online Reasoning and Cognitive Architecture), the first framework enabling active intelligence in video avatars. ORCA embodies Internal World Model (IWM) capabilities through two key innovations: (1) a closed-loop OTAR cycle (Observe-Think-Act-Reflect) that maintains robust state tracking under generative uncertainty by continuously verifying predicted outcomes against actual generations, and (2) a hierarchical dual-system architecture where System 2 performs strategic reasoning with state prediction while System 1 translates abstract plans into precise, model-specific action captions. By formulating avatar control as a POMDP and implementing continuous belief updating with outcome verification, ORCA enables autonomous multi-step task completion in open-domain scenarios. Extensive experiments demonstrate that ORCA significantly outperforms open-loop and non-reflective baselines in task success rate and behavioral coherence, validating our IWM-inspired design for advancing video avatar intelligence from passive animation to active, goal-oriented behavior.
DLVP-CLIP: Enhancing Fine-Grained Zero-Shot Anomaly Detection via Dynamic Local Visual Prompting
Authors: Gaowei Zhang; Lihe Zhang
Venue: CVPR 2026 (main)

Abstract: Zero-shot anomaly detection (ZSAD) aims to utilize auxiliary data to train models for generalized learning of unseen categories, which has important application value in fields such as industrial quality inspection and medical diagnosis. Although methods based on CLIP show potential, their pre-training objective of focusing on overall semantic alignment between images and text makes the model insensitive to local details, which is inherently contradictory to the need for fine-grained local features in anomaly detection. Existing improvement methods rely on predefined text prompt frameworks to perceive local information, but struggle to effectively address the issue of insufficient local perception. To address this, this paper proposes a dynamic local visual prompting method based on CLIP (DLVP-CLIP). DLVP dynamically identifies and extracts local visual features from key regions in images as prompt tokens using the Semantic-Aware Local Feature Selector (SLFS) module, and utilizes the multi-modal local prompt (MLoP) module to jointly optimize representations in both visual and textual spaces, achieving more precise cross-modal alignment. Additionally, the high-low frequency decomposition module (HFD) is introduced to separate and process global structural and local textural information via wavelet transformation, thereby enhancing detail perception. Extensive experiments on 13 anomaly detection datasets demonstrate that DLVP-CLIP achieves outstanding ZSAD performance on datasets from the industrial and medical domains.
Minimal Constraint Relaxation for Multiview Autocalibration
Authors: Norio Kosaka; Timothy Duff; Tomas Pajdla
Venue: CVPR 2026 (main)

Abstract: Polynomial systems in multiview geometry are often over-constrained, and naively removing equations can lead to unstable or inconsistent solutions. We revisit this issue through constraint relaxation---selectively removing equations to obtain a finite set of isolated (and, ideally, well-conditioned) solutions. Focusing on the three-view Kruppa equations for autocalibration, we introduce a minimal relaxation framework for identifying subsets of constraints leading to geometrically valid solutions. Using both symbolic computation and numerical homotopy continuation methods, we enumerate and classify all possible relaxation patterns arising from our formulation of Kruppa's equations, isolating all patterns that yield finitely-many solutions. In experiments with synthetic and real images, we demonstrate empirically that specific relaxations are well-conditioned and consistently outperform multiple baselines, including a more classical Kruppa formulation. Overall, our findings establish the minimal relaxation framework as a practical tool in multiview geometry computation.
SODA: Sensitivity-Oriented Dynamic Acceleration for Diffusion Transformer
Authors: Tong Shao; Yusen Fu; Guoying Sun; Jingde Kong; Zhuotao Tian; Jingyong Su
Venue: CVPR 2026 (main)

Abstract: Diffusion Transformers have become a dominant paradigm in visual generation, yet their low inference efficiency remains a key bottleneck hindering further advancement. Among common training-free techniques, caching offers high acceleration efficiency but often compromises fidelity, whereas pruning shows the opposite trade-off. Integrating caching with pruning achieves a balance between acceleration and generation quality. However, existing methods typically employ fixed and heuristic schemes to configure caching and pruning strategies. While they roughly follow the overall sensitivity trend of generation models to acceleration, they fail to capture fine-grained and complex variations, inevitably skipping highly sensitive computations and leading to quality degradation. Furthermore, such manually designed strategies exhibit poor generalization. To address these issues, we propose SODA, a Sensitivity-Oriented Dynamic Acceleration method that adaptively performs caching and pruning based on fine-grained sensitivity. SODA builds an offline sensitivity error modeling framework across timesteps, layers, and modules to capture the sensitivity to different acceleration operations. The cache intervals are optimized via dynamic programming with sensitivity error as the cost function, minimizing the impact of caching on model sensitivity. During pruning and cache reuse, SODA adaptively determines the pruning timing and rate to preserve computations of highly sensitive tokens, significantly enhancing generation fidelity. Extensive experiments on DiT-XL/2, PixArt-\alpha, and OpenSora demonstrate that SODA achieves state-of-the-art generation fidelity under controllable acceleration ratios. Our code is released publicly at: https://github.com/leaves162/SODA.
ClusterMark: Towards Robust Watermarking for Autoregressive Image Generators with Visual Token Clustering
Authors: Denis Lukovnikov; Andreas Müller; Erwin Quiring; Asja Fischer
Venue: CVPR 2026 (main)

Abstract: In-generation watermarking for latent diffusion models has recently shown high robustness in marking generated images for easier detection and attribution. However, its application to autoregressive (AR) image models is underexplored. Autoregressive models generate images by autoregressively predicting a sequence of visual tokens that are then decoded into pixels using a VQ-VAE decoder. Inspired by KGW watermarking for large language models, we examine token-level watermarking schemes that bias the next-token prediction based on prior tokens. We find that a direct transfer of these schemes works in principle, but the detectability of the watermarks decreases considerably under common image perturbations. As a remedy, we propose a watermarking approach based on visual token clustering, which assigns similar tokens to the same set (red or green). We investigate token clustering in a training-free setting, as well as in combination with a more accurate fine-tuned token or cluster predictor. Overall, our experiments show that cluster-based watermarks greatly improve robustness against perturbations and regeneration attacks while preserving image quality, outperforming a set of baselines and concurrent works. Moreover, our methods offer fast verification runtime, comparable to lightweight post-hoc watermarking techniques.
PhysHO: Physics-Based Dynamic 3D Gaussian Human and Object from Monocular Video
Authors: Suyi Jiang; Gim Hee Lee
Venue: CVPR 2026 (main)

Abstract: Physically plausible reconstruction of human-object dynamics from a single video remains under-explored in physics-based methods. Most prior approaches omit human-generated internal actuation by assuming motion driven solely by gravity and simple contacts. They also rely on idealized constitutive laws that underfit heterogeneous and anisotropic materials. We introduce PhysHO, which tightly couples SMPL-driven Linear Blend Skinning (LBS) with a Material Point Method (MPM) simulator to address these gaps. Our key insight is to use LBS as an interpretable actuation prior and MPM to propagate those forces through contact under physical constraints. Concretely, we derive targeted actuation with a PD controller guided by LBS trajectories and gate it per particle via a learnable LBS-impact factor so that only particles inside the SMPL volume are directly actuated. We model real materials with residual neural constitutive laws layered on expert elastic-plastic models and conditioned on per particle to capture heterogeneity and anisotropy. We stabilize monocular learning with structure-preserving 3D flow supervision and a progressive and loss-balanced training schedule. Our PhysHO reconstructs observed dynamics with high fidelity, and predicts future motion and simulates outcomes under novel human actions. Experimental results demonstrate robust human-driven interactions beyond gravity-only scenes.
CaST-Bench: Benchmarking Causal Chain-Grounded Spatio-Temporal Reasoning for Video Question Answering
Authors: Mingfang Zhang; Jingjing Pan; Ashutosh Kumar; Rajat Saini; Mustafa Erdogan; Hsuan-Kung Yang; Caixin Kang; Yifei Huang; Yoichi Sato; Quan Kong
Venue: CVPR 2026 (main)

Abstract: Cause-and-effect reasoning in video is a significant challenge for Vision-Language Models (VLMs), as it requires going beyond surface-level perception to a deeper understanding of causal mechanisms. However, existing benchmarks rarely provide the fine-grained, grounded evidence needed to rigorously evaluate this capability. To address this gap, we introduce CaST-Bench, a benchmark for Causal Chain-Grounded Spatio-Temporal Video Reasoning. CaST-Bench presents complex causal questions that require models to identify and localize a chain of multiple spatio-temporal evidences. Through a human-AI collaborative pipeline, we construct a high-quality dataset of 2,066 questions over 1,015 videos, with causal chains annotated by temporal segments and bounding-box tracks. Furthermore, we design a comprehensive evaluation suite with novel metrics that assess not only answer correctness but also the capability for visual evidence grounded reasoning. This grounding is crucial for improving accuracy by mitigating spurious correlations and for enhancing user trust by making models more transparent. Our experiments show that current VLMs struggle with causal questions, largely due to their limited ability to construct precise and grounded causal chains. This highlights an important direction for improving future VLMs.
The Coherence Trap: When MLLM-Crafted Narratives Exploit Manipulated Visual Contexts
Authors: Yuchen Zhang; Yaxiong Wang; Yujiao Wu; Lianwei Wu; Li Zhu; Zhedong Zheng
Venue: CVPR 2026 (main)

Abstract: The detection and grounding of multimedia manipulation has emerged as a critical challenge in combating AI-generated disinformation. While existing methods have made progress in recent years, we identify two fundamental limitations in current approaches: (1) Underestimation of MLLM-driven deception risk: prevailing techniques primarily address rule-based text manipulations, yet fail to account for sophisticated misinformation synthesized by multimodal large language models (MLLMs) that can dynamically generate semantically coherent, contextually plausible yet deceptive narratives conditioned on manipulated images; (2) Unrealistic misalignment artifacts: currently focused scenarios rely on artificially misaligned content that lacks semantic coherence, rendering them easily detectable. To address these gaps holistically, we propose a new adversarial pipeline that leverages MLLMs to generate high-risk disinformation. Our approach begins with constructing the MLLM-Driven Synthetic Multimodal (MDSM) dataset, where images are first altered using state-of-the-art editing techniques and then paired with MLLM-generated deceptive texts that maintain semantic consistency with the visual manipulations. Building upon this foundation, we present the Artifact-aware Manipulation Diagnosis via MLLM (AMD) framework featuring two key innovations: Artifact Pre-perception Encoding strategy and Manipulation-Oriented Reasoning, to tame MLLMs for the MDSM problem. Comprehensive experiments validate our framework's superior generalization capabilities as a unified architecture for detecting MLLM-powered multimodal deceptions. In cross-domain testing on the MDSM dataset, AMD achieves the best average performance, with ACC, mAP, and mIoU scores of 88.18, 60.25, and 61.02, respectively.
Generative Diffusion Priors for 3D Mapping of the Dark Universe
Authors: Brandon Zhao; Diana Scognamiglio; Olivier Doré; Katherine L. Bouman
Venue: CVPR 2026 (main)

Abstract: Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in cosmology. Unlike standard 3D reconstruction with multiple viewpoints, we observe the universe from a single line of sight, through noisy shape distortions of galaxies with uncertain distances, so meaningful recovery of the 3D matter field requires strong prior assumptions. Existing methods either produce point estimates with handcrafted priors or use neural ensembles for approximate Bayesian uncertainty, and struggle to capture the non-Gaussian, filamentary structure of the cosmic web. With the advent of new high-resolution cosmological simulations, we now have an alternative source of prior knowledge that captures the nonlinear statistics of structure formation with far greater fidelity than analytic prescriptions. We leverage these simulations to build a new dataset \texttt Conicus3D , which enables us to learn a data-driven diffusion-model prior capturing the full 3D distribution of dark matter structure across cosmic time. Building on recent plug-and-play approaches, we modify a diffusion-based posterior sampling scheme to the 3D weak-lensing setting, combining the learned prior with a differentiable physical forward model. On realistic simulations targeting a modern weak lensing survey, our approach yields substantially improved 2D and 3D reconstruction accuracy over baseline methods. Moreover, it produces posterior samples whose statistics closely track the underlying simulations, while remaining robust to moderate shifts in cosmology.
PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching
Authors: Hanqiao Ye; Yuzhou Liu; Yangdong Liu; Shuhan Shen
Venue: CVPR 2026 (main)

Abstract: While structure-based relocalizers have long strived for point correspondences when establishing or regressing query-map associations, in this paper, we pioneer the use of planar primitives and 3D planar maps for lightweight 6-DoF camera relocalization in structured environments. Planar primitives, beyond being fundamental entities in projective geometry, also serve as region-based representations that encapsulate both structural and semantic richness. This motivates us to introduce PlanaReLoc, a streamlined plane-centric paradigm where a deep matcher associates planar primitives across the query image and the map within a learned unified embedding space, after which the 6-DoF pose is solved and refined under a robust framework. Through comprehensive experiments on the ScanNet and 12Scenes datasets across hundreds of scenes, our method demonstrates the superiority of planar primitives in facilitating reliable cross-modal structural correspondences and achieving effective camera relocalization without requiring realistically textured/colored maps, pose priors, or per-scene training. The code and data are available at https://github.com/3dv-casia/PlanaReLoc.
Generalizable Knowledge Distillation from Vision Foundation Models for Semantic Segmentation
Authors: Chonghua Lv; Dong Zhao; Shuang Wang; Dou Quan; Ning Huyan; Nicu Sebe; Zhun Zhong
Venue: CVPR 2026 (main)

Abstract: Knowledge distillation (KD) has been widely applied in semantic segmentation to compress large models, but conventional approaches primarily preserve in-domain accuracy while neglecting out-of-domain generalization, which is essential under distribution shifts. This limitation becomes more severe with the emergence of vision foundation models (VFMs): although VFMs exhibit strong robustness on unseen data, distilling them with conventional KD often compromises this ability. We propose Generalizable Knowledge Distillation (GKD), a multi-stage framework that explicitly enhances generalization. GKD decouples representation learning from task learning. In the first stage, the student acquires domain-agnostic representations through selective feature distillation, and in the second stage, these representations are frozen for task adaptation, thereby mitigating overfitting to visible domains. To further support transfer, we introduce a query-based soft distillation mechanism, where student features act as queries to teacher representations to selectively retrieve transferable spatial knowledge from VFMs. Extensive experiments on five domain generalization benchmarks demonstrate that GKD consistently outperforms existing KD methods, achieving average gains of +1.9% in foundation-to-foundation (F2F) and +10.6% in foundation-to-local (F2L) distillation. The code will be available at https://github.com/Younger-hua/GKD.