GenAI4ELab/papercli-papers huggingface dataset:
DocPrune: Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning
Authors: Joonmyung Choi; Sanghyeok Lee; Jongha Kim; Sehyung Kim; Dohwan Ko; Jihyung Kil; Hyunwoo J. Kim
Venue: CVPR 2026 (main)

Abstract: Recent advances in vision-language models have shown strong performance across diverse multimodal tasks, including document question answering that leverages structured visual cues from text, tables, and figures. However, unlike natural images, document images contain large backgrounds and only sparse supporting evidence, leading to the waste of substantial computational resources, especially for long documents. We observe that existing token reduction methods for natural images and videos fall short in utilizing the structural sparsity unique to documents. To address this, we propose DOCPRUNE, a training-free document token pruning framework designed for efficient long document understanding. The proposed method preserves only the essential tokens for the task while removing unnecessary ones, such as background or question-irrelevant tokens. Moreover, it automatically selects the appropriate layers to initiate token pruning based on the model's level of comprehension. Our experiments on the M3DocRAG benchmark show that DOCPRUNE improves throughput by 3.0x and 3.3x in the encoder and decoder, respectively, while boosting the F1 score by +1.0, achieving both higher accuracy and efficiency without any additional training.
Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration
Authors: Jun Li; Lizhi Xiong; Ziqiang Li; Weiwei Jiang; Zhangjie Fu; Yong Li; Guo-Sen Xie
Venue: CVPR 2026 (main)

Abstract: Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in large-scale training datasets.Existing concept erasure methods, whether text-only or image-assisted, face trade-offs: textual approaches often fail to fully suppress concepts, while naive image-guided methods risk over-erasing unrelated content. We propose TICoE, a Text-Image Collaborative Erasing framework that achieves precise and faithful concept removal through a continuous convex concept manifold and hierarchical visual representation learning. TICoE precisely removes target concepts while preserving unrelated semantic and visual content. To objectively assess the quality of erasure, we further introduce a fidelity-oriented evaluation strategy that measures post-erasure usability. Experiments on multiple benchmarks show that TICoE surpasses prior methods in concept removal precision and content fidelity, enabling safer, more controllable text-to-image generation.
POUR: A Provably Optimal Method for Unlearning Representation via Neural Collapse
Authors: Anjie Le; Can Peng; Yuyuan Liu; J. Alison Noble
Venue: CVPR 2026 (main)

Abstract: In computer vision, machine unlearning aims to remove the influence of specific visual concepts or training images without retraining from scratch. Studies show that existing approaches often modify the classifier while leaving internal representations intact, resulting in incomplete forgetting.In this work, we extend the notion of unlearning to the representation level, deriving a three-term interplay between forgetting efficacy, retention fidelity, and class separation. Building on Neural Collapse theory, we show that the orthogonal projection of a simplex Equiangular Tight Frame (ETF) remains an ETF in a lower-dimensional space, yielding a provably optimal forgetting operator.We further introduce the Representation Unlearning Score (RUS) to quantify representation-level forgetting and retention fidelity. Building on this, we introduce POUR (Provably Optimal Unlearning of Representations), a geometric projection method with closed-form (POUR-P) and feature-level unlearning variants under a distillation scheme (POUR-D).Experiments on CIFAR-10/100 and PathMNIST demonstrate that POUR achieves effective unlearning while preserving retained knowledge, outperforming state-of-the-art unlearning methods on both classification-level and representation-level metrics. Code will be released upon acceptance of the paper.
FlowMotion: Training-Free Flow Guidance for Video Motion Transfer
Authors: Zhen Wang; Youcan Xu; Jun Xiao; Long Chen
Venue: CVPR 2026 (main)

Abstract: Video motion transfer aims to generate a target video that inherits motion patterns from a source video while rendering new scenes. Existing training-free approaches focus on constructing motion guidance based on the intermediate outputs of pre-trained T2V models, which results in heavy computational overhead and limited flexibility. In this paper, we present FlowMotion, a novel training-free framework that enables efficient and flexible motion transfer by directly leveraging the predicted outputs of flow-based T2V models. Our key insight is that early latent predictions inherently encode rich temporal information. Motivated by this, we propose flow guidance, which extracts motion representations based on latent predictions to align motion patterns between source and generated videos. We further introduce a velocity regularization strategy to stabilize optimization and ensure smooth motion evolution. By operating purely on model predictions, FlowMotion achieves superior time and resource efficiency as well as competitive performance compared with state-of-the-art methods.
Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning
Authors: Yifei Li; Wenzhao Zheng; Yanran Zhang; Runze Sun; Yu Zheng; Lei Chen; Jie Zhou; Jiwen Lu
Venue: CVPR 2026 (main)

Abstract: The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations.We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. To comprehensively evaluate Skyra, we introduce ViF-Bench, a benchmark comprising 3K high-quality samples generated by over ten state-of-the-art video generators. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, while our evaluation yields valuable insights for advancing explainable AI-generated video detection. Our code, models, and datasets will be made publicly available.
Visual Diffusion Models are Geometric Solvers
Authors: Nir Goren; Shai Yehezkel; Omer Dahary; Andrey Voynov; Or Patashnik; Daniel Cohen-Or
Venue: CVPR 2026 (main)

Abstract: In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by working in pixel space. We first demonstrate this on the Inscribed Square Problem, a long-standing problem in geometry that asks whether every Jordan curve contains four points forming a square. We then extend the approach to two other well-known hard geometric problems: the Steiner Tree Problem and the Maximum Area Polygon Problem. Our method treats each problem instance as an image and trains a standard visual diffusion model that transforms Gaussian noise into an image representing a valid approximate solution that closely matches the exact one. The model learns to transform noisy geometric structures into correct configurations, effectively recasting geometric reasoning as image generation. Unlike prior work that necessitates specialized architectures and domain-specific adaptations when applying diffusion to parametric geometric representations, we employ a standard visual diffusion model that operates on the visual representation of the problem. This simplicity highlights a surprising bridge between generative modeling and geometric problem solving. Beyond the specific problems studied here, our results point toward a broader paradigm: operating in image space provides a general and practical framework for approximating notoriously hard problems, and opens the door to tackling a far wider class of challenging geometric tasks.
Image Diffusion Preview with Consistency Solver
Authors: Fu-Yun Wang; Hao Zhou; Liangzhe Yuan; Sanghyun Woo; Boqing Gong; Bohyung Han; Ming-Hsuan Yang; Han Zhang; Yukun Zhu; Ting Liu; Long Zhao
Venue: CVPR 2026 (main)

Abstract: The slow inference process of image diffusion models significantly degrades interactive user experiences. To address this, we introduce Diffusion Preview, a novel paradigm employing rapid, low-step sampling to generate preliminary outputs for user evaluation, deferring full-step refinement until the preview is deemed satisfactory. Existing acceleration methods, including training-free solvers and post-training distillation, struggle to deliver high-quality previews or ensure consistency between previews and final outputs. In this paper, we propose ConsistencySolver derived from general linear multistep methods, a lightweight, trainable high-order solver optimized via Reinforcement Learning, that enhances preview quality and consistency.Experimental results demonstrate that ConsistencySolver significantly improves generation quality in low-step scenarios, making it ideal for efficient preview-and-refine workflows. Notably, it achieves FID scores on-par with Multistep DPM-Solver using 47% fewer steps, while outperforming distillation baselines. Furthermore, user studies indicate our approach reduces overall user interaction time by nearly 50% while maintaining generation quality.
LATTICE: Democratize High-Fidelity 3D Generation at Scale
Authors: Zeqiang Lai; Yunfei Zhao; Zibo Zhao; Haolin Liu; Qingxiang Lin; Jingwei Huang; Chunchao Guo; Xiangyu Yue
Venue: CVPR 2026 (main)

Abstract: We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis benefits from fixed spatial grids and well-established transformer architectures, 3D generation remains fundamentally more challenging due to the need to predict both spatial structure and detailed geometric surfaces from scratch. These challenges are exacerbated by the computational complexity of existing 3D representations and the lack of structured and scalable 3D asset encoding schemes. To address this, we propose VoxSet, a semi-structured representation that compresses 3D assets into a compact set of latent vectors anchored to a coarse voxel grid, enabling efficient and position-aware generation. VoxSet retains the simplicity and compression advantages of prior VecSet methods while introducing explicit structure into the latent space, allowing positional embeddings to guide generation and enabling strong token-level test-time scaling. Built upon this representation, LATTICE adopts a two-stage pipeline: first generating a sparse voxelized geometry anchor, then producing detailed geometry using a recitified flow transformer. Our method is simple at its core, but supports arbitrary resolution decoding, low-cost training, and flexible inference schemes, achieving state-of-the-art performance on various aspects, and offering a significant step toward scalable, high-quality 3D asset creation.
A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens
Authors: Tommie Kerssies; Gabriele Berton; Ju He; Qihang Yu; Wufei Ma; Daan de Geus; Gijs Dubbelman; Liang-Chieh Chen
Venue: CVPR 2026 (main)

Abstract: Anticipating diverse future states is a central challenge in video world modeling. Discriminative world models produce deterministic predictions that implicitly average over possible futures, while existing generative world models remain computationally expensive. Recent work demonstrates that predicting the future in the feature space of a vision foundation model (VFM), rather than a latent space optimized for pixel reconstruction, requires significantly fewer world model parameters. However, most such approaches remain discriminative. In this work, we introduce DeltaWorld, a generative VFM-based world model that efficiently generates diverse plausible futures. At the core of DeltaWorld is DeltaTok, a tokenizer that encodes the feature difference between consecutive frames into a single continuous "delta" token, reducing video from a three-dimensional spatio-temporal representation to a one-dimensional temporal sequence. For example, this yields a 1,024x token reduction with 512x512 frames. Delta tokens enable efficient and effective multi-hypothesis training, where many diverse futures are generated in parallel and only the best is supervised. At inference, this leads to diverse predictions in a single forward pass. Experiments on dense forecasting tasks demonstrate that DeltaWorld forecasts futures that more closely align with real-world outcomes, while having over 35x fewer parameters and using 2,000x fewer FLOPs than existing generative world models. Project page: https://deltatok.github.io.
Resolving the Identity Crisis in Text-to-Image Generation
Authors: Shubhankar Borse; Farzad Farhadzadeh; Munawar Hayat; Fatih Porikli
Venue: CVPR 2026 (main)

Abstract: State-of-the-art text-to-image models suffer from a persistent identity crisis when generating scenes with multiple humans: producing duplicate faces, merging identities, and miscounting individuals. We present DisCo (Reinforcement with Diversity Constraints), a reinforcement learning framework that directly optimizes identity diversity both within images and across groups of generated samples. DisCo fine-tunes flow-matching models using Group-Relative Policy Optimization (GRPO), guided by a compositional reward that: (i) penalizes facial similarity within images, (ii) discourages identity repetition across samples, (iii) enforces accurate person counts, and (iv) preserves visual fidelity and prompt alignment via human preference scores. A single-stage curriculum stabilizes training as prompt complexity increases. Importantly, this method does not require any real data. On the DiverseHumans Testset, DisCo achieves 98.6% Unique Face Accuracy and near-perfect Global Identity Spread, outperforming open-source and proprietary models (e.g., Gemini, GPT-Image) while maintaining perceptual quality. Our results establish cross-sample diversity as a critical axis for resolving identity collapse, positioning DisCo as a scalable, annotation-free solution for multi-human image synthesis. Project page: https://qualcomm-ai-research.github.io/disco/.
Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy
Authors: Teng Hu; Zhentao Yu; Guozhen Zhang; Zihan Su; Zhengguang Zhou; Youliang Zhang; Yuan Zhou; Qinglin Lu; Ran Yi
Venue: CVPR 2026 (main)

Abstract: The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reveals that this issue is rooted in three fundamental challenges of the joint diffusion process: (1) Correspondence Drift, where concurrently evolving noisy latents impede stable learning of alignment; (2) inefficient global attention mechanisms that fail to capture fine-grained temporal cues; and (3) the intra-modal bias of conventional Classifier-Free Guidance (CFG), which enhances conditionality but not cross-modal synchronization.To overcome these challenges, we introduce Harmony, a novel framework that mechanistically enforces audio-visual synchronization. We first propose a Cross-Task Synergy training paradigm to mitigate drift by leveraging strong supervisory signals from audio-driven video and video-driven audio generation tasks. Then, we design a Global-Local Decoupled Interaction Module for efficient and precise temporal-style alignment. Finally, we present a novel Synchronization-Enhanced CFG (SyncCFG) that explicitly isolates and amplifies the alignment signal during inference. Extensive experiments demonstrate that Harmony establishes a new state-of-the-art, significantly outperforming existing methods in both generation fidelity and, critically, in achieving fine-grained audio-visual synchronization.
MAMMA: Markerless Accurate Multi-person Motion Acquisition
Authors: Hanz Cuevas Velasquez; Anastasios Yiannakidis; Soyong Shin; Giorgio Becherini; Markus Höschle; Joachim Tesch; Taylor Obersat; Tsvetelina Alexiadis; Eni Halilaj; Michael J. Black
Venue: CVPR 2026 (main)

Abstract: We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video. Traditional motion-capture systems rely on physical markers. Although they offer high accuracy, their requirements of specialized hardware, manual marker placement, and extensive post-processing make them costly and time-consuming. Recent learning-based methods attempt to overcome these limitations, but most are designed for single-person capture, rely on sparse keypoints, or struggle with occlusions and physical interactions. In this work, we introduce a method that predicts dense 2D contact-aware and visibility-aware surface landmarks conditioned on segmentation masks, enabling person-specific correspondence estimation even under heavy occlusion. We employ a novel architecture that exploits learnable queries for each landmark. We demonstrate that our approach can handle complex person--person interaction and offers greater accuracy than existing methods. To train our network, we construct a large, synthetic multi-view dataset combining human motions from diverse sources, including extreme poses, hand motions, and close interactions. Our dataset yields high-variability synthetic sequences with rich body contact and occlusion, and includes SMPL-X ground-truth annotations with dense 2D landmarks. The result is a system capable of accurately capturing human motion without the need for markers. Our approach offers competitive reconstruction quality compared to commercial marker-based motion-capture solutions, without the extensive manual cleanup. Finally, we address the absence of common benchmarks for dense-landmark prediction and markerless motion capture by introducing two evaluation settings built from real multi-view sequences. https://mamma.is.tue.mpg.de/
Towards Robust Vision Transformers: Path Dependency Analysis and a Simple Two-Stage Adversarial Training
Authors: Seongmin Kim; Byung Cheol Song
Venue: CVPR 2026 (main)

Abstract: The Vision Transformer (ViT) has surpassed Convolutional Neural Networks (CNNs) in performance, becoming the de facto architecture in modern computer vision. However, despite its superior representational capacity, research on the adversarial robustness of ViTs remains limited, with most studies still biased toward CNN-based models. This work aims to address this architectural bias and conduct an in-depth analysis of the interaction between ViTs and adversarial training (AT).We first show that ViTs can identify semantic components of objects through their class attention maps, indicating that adversarially trained ViTs inherently encode strong semantic priors. Next, using the proposed Gradient Path Masking (GPM) analysis, we examine the internal information flow of ViTs and verify that the residual path serves as a major bottleneck that provides advantageous information to adversaries. Furthermore, our inter-patch relation analysis reveals that adversarially trained ViTs tend to rely more on global than local relationships in early layers--a novel observation suggesting a fundamental potential incompatibility between ViTs and hybrid architectures that inject CNN-style inductive biases.Building upon these findings, we design a simple yet effective two-stage AT scheme to mitigate this structural incompatibility, achieving simultaneous improvements in robustness and generalization across various ViT variants and training methods. The proposed method is compatible with a wide range of AT frameworks and models.
First Frame Is the Place to Go for Video Content Customization
Authors: Jingxi Chen; Zongxia Li; Zhichao Liu; Guangyao Shi; Xiyang Wu; Fuxiao Liu; Cornelia Fermüller; Brandon Y. Feng; Yiannis Aloimonos
Venue: CVPR 2026 (main)

Abstract: What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation. In this work, we reveal a fundamentally different perspective: video models implicitly treat the first frame as a conceptual memory buffer that stores visual entities for later reuse during generation. Leveraging this insight, we show that it's possible to achieve robust and generalized video content customization in diverse scenarios, using only 20-50 training examples without architectural changes or large-scale finetuning. This unveils a powerful, overlooked capability of video generation models for reference-based video customization.
High-Quality and Efficient Turbulence Mitigation with Events
Authors: Xiaoran Zhang; Jian Ding; Yuxing Duan; Haoyue Liu; Gang Chen; Yi Chang; Luxin Yan
Venue: CVPR 2026 (main)

Abstract: Turbulence mitigation (TM) is highly ill-posed due to the stochastic nature of atmospheric turbulence. Most methods rely on multiple frames recorded by conventional cameras to capture stable patterns in natural scenarios. However, they inevitably suffer from a trade-off between accuracy and efficiency: more frames enhance restoration at the cost of higher system latency and larger data overhead. Event cameras, equipped with microsecond temporal resolution and efficient sensing of dynamic changes, offer an opportunity to break the bottleneck. In this work, we present EHETM, a high-quality and efficient TM method inspired by the superiority of events to model motions in continuous sequences. We discover two key phenomena: (1) turbulence-induced events exhibit distinct polarity alternation correlated with sharp image gradients, providing structural cues for restoring scenes; and (2) dynamic objects form spatiotemporally coherent "event tubes" in contrast to irregular patterns within turbulent events, providing motion priors for disentangling objects from turbulence. Based on these insights, we design two complementary modules that respectively leverage polarity-weighted gradients for scene refinement and event-tube constraints for motion decoupling, achieving high-quality restoration with few frames. Furthermore, we construct two real-world event-frame turbulence datasets covering atmospheric and thermal cases. Extensive experiments show that EHETM outperforms SOTA methods, especially under scenes with dynamic objects, while reducing data overhead and system latency by approximately 77.3% and 89.5%, respectively.