GenAI4ELab/papercli-papers huggingface dataset:
gQIR: Generative Quanta Image Reconstruction
Authors: Aryan Garg; Sizhuo Ma; Mohit Gupta
Venue: CVPR 2026 (main)

Abstract: Capturing high-quality images from only a few detected photons is a fundamental challenge in computational imaging. Single-photon avalanche diode (SPAD) sensors promise high-quality imaging in regimes where conventional cameras fail, but raw quanta frames contain only sparse, noisy, binary photon detections. Recovering a coherent image from a burst of such frames requires handling alignment, denoising, and demosaicing (for color) under noise statistics far outside those assumed by standard restoration pipelines or modern generative models. We present an approach that adapts large text-to-image latent diffusion models to the photon-limited domain of quanta burst imaging. Our method leverages the structural and semantic priors of internet-scale diffusion models while introducing mechanisms to handle Bernoulli photon statistics. By integrating latent-space restoration with burst-level spatio-temporal reasoning, our approach produces reconstructions that are both photometrically faithful and perceptually pleasing, even under high-speed motion. We evaluate the method on synthetic benchmarks and new real-world datasets, including the first color SPAD burst dataset and a challenging Deforming (XD) video benchmark. Across all settings, the approach substantially improves perceptual quality over classical and modern learning-based baselines, demonstrating the promise of adapting large generative priors to extreme photon-limited sensing. Code at https://github.com/Aryan-Garg/gQIR.
DuetMerging: Synergizing Dynamic and Static Strategies for Mitigating Task Interference in Model Merging
Authors: Yan Li; Guiping Cao; Yaguang Song; Ming Tao; Haoran Gong; Junhui Liu; Yaowei Wang; Dongmei Jiang
Venue: CVPR 2026 (main)

Abstract: Model merging offers a promising paradigm for consolidating multiple expert models into a single multitask architecture. However, its effectiveness is often hindered by task interference, where conflicting parameter updates from different tasks degrade performance. While dynamic, Mixture-of-Experts based methods have improved adaptability, they are fundamentally limited by constructing their expert pools from task vectors in isolation, failing to resolve underlying structural conflicts across tasks. In this paper, we introduce DuetMerging, a novel framework that synergistically mitigates task interference from both dynamic and static perspectives. Dynamically, we apply Tucker decomposition to a unified tensor of task vectors, creating a harmonized expert pool derived from a shared core tensor that structurally enhances synergies and suppresses conflicts. Statically, we introduce a neuron-based sparsification technique that leverages task-specific neuron activation patterns to create a precise mask. This allows us to selectively preserve critical information from the decomposition's residual while suppressing functionally irrelevant or conflicting parameters. Comprehensive experiments demonstrate that DuetMerging outperforms existing methods, establishing a new state-of-the-art in both task performance and parameter efficiency.
MatSpray: Fusing 2D Material World Knowledge on 3D Geometry
Authors: Philipp Langsteiner; Jan-Niklas Dihlmann; Hendrik Lensch
Venue: CVPR 2026 (main)

Abstract: Manual modeling of material parameters and 3D geometry is a time consuming yet essential task in the gaming and film industries. While recent advances in 3D reconstruction have enabled accurate approximations of scene geometry and appearance, these methods often fall short in relighting scenarios due to the lack of precise, spatially varying material parameters. At the same time, diffusion models operating on 2D images have shown strong performance in predicting physically based rendering (PBR) properties such as albedo, roughness, and metallicity. However, transferring these 2D material maps onto reconstructed 3D geometry remains a significant challenge. We propose a framework for fusing 2D material data into 3D geometry using a combination of novel learning-based and projection-based approaches. We begin by reconstructing scene geometry via Gaussian Splatting. From the input images, a diffusion model generates 2D maps for albedo, roughness, and metallic parameters. Any existing diffusion model that can convert images or videos to PBR materials can be applied. The predictions are further integrated into the 3D representation either by optimizing an image-based loss or by directly projecting the material parameters onto the Gaussians using Gaussian ray tracing. To enhance fine-scale accuracy and multi-view consistency, we further introduce a light-weight neural refinement step (Neural Merger), which takes ray-traced material features as input and produces detailed adjustments. Our results demonstrate that the proposed methods outperform existing techniques in both quantitative metrics and perceived visual realism. This enables more accurate, relightable, and photorealistic renderings from reconstructed scenes, significantly improving the realism and efficiency of asset creation workflows in content production pipelines.
ViRC: Enhancing Visual Interleaved Mathematical CoT with Reason Chunking
Authors: Lihong Wang; Liangqi Li; Weiwei Feng; Jiamin Wu; Changtao Miao; Tieru Wu; Rui Ma; Bo Zhang; Zhe Li
Venue: CVPR 2026 (main)

Abstract: CoT has significantly enhanced the reasoning ability of LLMs while it faces challenges when extended to multimodal domains, particularly in mathematical tasks.Existing MLLMs typically perform textual reasoning solely from a single static mathematical image, overlooking dynamic visual acquisition during reasoning.In contrast, humans repeatedly examine visual image and employ step-by-step reasoning to prove intermediate propositions.This strategy of decomposing the problem-solving process into key logical nodes adheres to Miller's Law in cognitive science.Inspired by this insight, we propose a **ViRC** framework for multimodal mathematical tasks, introducing a **Reason Chunking** mechanism that structures multimodal mathematical CoT into consecutive **Critical Reasoning Units (CRUs)** to simulate human expert problem-solving patterns.CRUs ensure intra-unit textual coherence for intermediate proposition verification while integrating visual information across units to generate subsequent propositions and support structured reasoning.To this end, we present **CRUX** dataset by using three visual tools and four reasoning patterns to provide explicitly annotated CRUs across multiple reasoning paths for each mathematical problem.Leveraging the CRUX dataset, we propose a progressive training strategy inspired by human cognitive learning, which includes Instructional SFT, Practice SFT, and Strategic RL, aimed at further strengthening the Reason Chunking ability of the model.The resulting **ViRC-7B** model achieves a 18.8% average improvement over baselines across multiple mathematical benchmarks.The codes will be made publicly available.
FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution
Authors: Yidi Liu; Zihao Fan; Jie Huang; Jie Xiao; Dong Li; Wenlong Zhang; LEI BAI; Xueyang Fu; Zheng-jun Zha
Venue: CVPR 2026 (main)

Abstract: Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) in image generation, recent work has adapted reward-based learning to image super-resolution (ISR) by using Image Quality Assessment (IQA) models as rewards. However, existing IQA models typically output only a single global score and are insensitive to local, fine-grained distortions, allowing perceptually undesirable artifacts to obtain spuriously high rewards and leading to reward hacking. To address this issue, we propose FinPercep-RM, a fine-grained perceptual reward model built on an encoder-decoder architecture that predicts both a global quality score and a Perceptual Degradation Map for spatially localizing and quantifying local defects. We further introduce FGR-30k, a dataset containing diverse and subtle distortions produced by real-world super-resolution models, to train the reward model. While FinPercep-RM provides stronger supervision, its increased complexity also makes generator policy learning unstable. We therefore develop a Co-evolutionary Curriculum Learning (CCL) strategy, in which the reward model and the ISR model evolve synchronously: the reward signal progressively increases in complexity, while the ISR model starts with simple global supervision for fast convergence and gradually transitions to fine-grained rewards. This easy-to-hard design stabilizes training and suppresses reward hacking. Extensive experiments across multiple ISR models demonstrate improvements in both global quality and local realism. Code will be available at https://github.com/lyd-2022/FinPercep-RM.
UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes
Authors: Yixun Liang; Kunming Luo; Xiao Chen; Rui Chen; Hongyu Yan; Weiyu Li; Jiarui Liu; Fei-Peng Tian; Ping Tan
Venue: CVPR 2026 (main)

Abstract: We present UniTEX, a novel two-stage 3D texture generation framework to create high-quality, consistent textures for 3D assets. Existing approaches predominantly rely on UV-based models in the second stage to refine textures after reprojecting the generated multi-view images onto the 3D shapes, which introduces challenges related to topological ambiguity. To address this, we bypass the limitations of UV mapping by introducing a Large Texturing Model (LTM) that directly regresses textures in a unified 3D functional space. Moreover, to enable more effective and complete supervision of LTM, we propose to extend surface-defined textures into a continuous volumetric field to serve as an advanced training objective, which we refer to as Texture Functions (TF). Finally, we develop an advanced LoRA-based strategy for efficiently adapting large-scale 2D Diffusion Transformers (DiTs) for high-quality multi-view texture synthesis as our first stage. Extensive experiments demonstrate that UniTEX achieves superior visual quality and texture integrity compared to existing approaches, offering a generalizable and scalable solution for automated 3D texture generation. Code is available in: https://github.com/HKUST-SAIL/UniTEX
ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning
Authors: Shengyuan Ding; Xinyu Fang; Ziyu Liu; Yuhang Zang; Yuhang Cao; Xiangyu Zhao; Haodong Duan; Xiaoyi Dong; Jianze Liang; Bin Wang; Conghui He; Dahua Lin; Jiaqi Wang
Venue: CVPR 2026 (main)

Abstract: Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to use tools for verification, limiting their reliability on complex multimodal reasoning tasks.We present **ARM-Thinker**, an **A**gentic multimodal **R**eward **M**odel that autonomously invokes external tools (e.g., image cropping, doc page retrieval) to ground judgments in verifiable evidence, replacing static, non-interactive reward scoring.This enables the model to verify fine-grained visual details, cross-reference multi-page evidence, and validate reasoning claims, which are capabilities absent in existing reward models.We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy.To evaluate agentic reward modeling, we introduce **ARMBench-VL**, comprising three benchmarks that assess fine-grained visual grounding (image-level tools), multi-page document understanding (retrieval tools), and instruction following (text-level verification).ARM-Thinker achieves +16.2% average improvement on reward modeling benchmarks, +9.6% on tool-use tasks, and outperforms baselines on multimodal math and logical reasoning benchmarks.Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.
Denoising as Path Planning: Training-Free Acceleration of Diffusion Models with DPCache
Authors: Bowen Cui; Yuanbin Wang; Huajiang Xu; Biaolong Chen; Aixi Zhang; Hao Jiang; Zhengzheng Jin; Xu Liu; Pipei Huang
Venue: CVPR 2026 (main)

Abstract: Diffusion models have demonstrated remarkable success in image and video generation, yet their practical deployment remains hindered by the substantial computational overhead of multi-step iterative sampling. Among acceleration strategies, caching-based methods offer a training-free and effective solution by reusing or predicting features across timesteps. However, existing approaches rely on fixed or locally adaptive schedules without considering the global structure of the denoising trajectory, often leading to error accumulation and visual artifacts. To overcome this limitation, we propose DPCache, a novel training-free acceleration framework that formulates diffusion sampling acceleration as a global path planning problem. DPCache constructs a Path-Aware Cost Tensor from a small calibration set to quantify the path-dependent error of skipping timesteps conditioned on the preceding key timestep. Leveraging this tensor, DPCache employs dynamic programming to select an optimal sequence of key timesteps that minimizes the total path cost while preserving trajectory fidelity. During inference, the model performs full computations only at these key timesteps, while intermediate outputs are efficiently predicted using cached features. Extensive experiments on DiT, FLUX, and HunyuanVideo demonstrate that DPCache achieves strong acceleration with minimal quality loss, outperforming prior acceleration methods by +0.031 ImageReward at 4.87x speedup and even surpassing the full-step baseline by +0.028 ImageReward at 3.54x speedup on FLUX, validating the effectiveness of our path-aware global scheduling framework. Code is available at https://github.com/argsss/DPCache.
GPFlow: Gaussian Prototype Probability Flow for Unsupervised Multi-Modal Anomaly Detection
Authors: Yiting Li; Xulei Yang; Jingyi Liao; Jing Zhang; Fayao Liu
Venue: CVPR 2026 (main)

Abstract: In this paper, we study unsupervised multi-modal anomaly detection under challenging few-shot conditions, where only a few normal training samples are available for each class. To prevent the trivial reconstruction of anomalies, recent methods often rely on discrete prototypes to establish an information bottleneck. However, such discrete prototypes fail to capture continuous and anisotropic variations of normal features.   We therefore propose GPFlow, a probability-flow-inspired framework that models normality using learnable Gaussian prototypes. The key component of GPFlow is an analytical Posterior-Mean Path (PMP) router, which reconstructs features through the posterior mean of a noise-smoothed Gaussian mixture. This yields anisotropic shrinkage as a covariance-aware information bottleneck: PMP selectively preserves normal variations aligned with the covariance structure of Gaussian prototypes while strictly suppressing deviations inconsistent with the prototypes. To exploit complementary knowledge across modalities, GPFlow further combines intra-modal and cross-modal reconstruction, and applies a lightweight instance-aware prior calibration to alleviate the distribution mismatch between sparse training data and diverse test samples. Experiments on MVTec-3D-AD and Eyecandies show that GPFlow achieves significant performance improvement with only a few normal training samples while remaining computationally efficient.
MVLM: Template-Free Tracking via Vision-Language Margin Confidence and Memory-Gated Tracking
Authors: Dae-Hyeon Park; Mina Baek; Jeong-Hun Ha; Chan-Seop Park; Jamshidjon Ganiev; Seung-Hwan Bae
Venue: CVPR 2026 (main)

Abstract: We introduce a new template-free tracking paradigm based solely on natural language, capable of tracking an arbitrary object and seamlessly switching to a new target without box initialization.Our key idea is to localize an object via vision-language (VL) correlation.However, using the correlation alone is brittle under large search regions due to spatial uncertainty and ambiguous VL saliency. To resolve these, we propose MVLM, a memory-based vision-language margin confidence that integrates vision-language correlation, encoder prediction, and temporal memory.MVLM dynamically gates the search region--switching between compact ROI (Region of Interest) search and global re-localization--to reduce spatial uncertainty. Theoretically, we derive bounds that connect the MVLM score to tracking probability, characterizing mis-localization within ROI and ROI-exclusion probabilities.Through extensive evaluation, we validate our theorems and achieve state-of-the-art performance on several benchmarks (TNL2K, LaSOT, OTB99 and MGIT) using only language guidance.
Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens
Authors: Zeyuan Yang; Xueyang Yu; Delin Chen; Maohao Shen; Chuang Gan
Venue: CVPR 2026 (main)

Abstract: Vision-language models (VLMs) excel at multimodal understanding, yet their text-only decoding forces them to verbalize visual reasoning, limiting performance on tasks that demand visual imagination. Recent attempts train VLMs to render explicit images, but the heavy image-generation pre-training often hinders the reasoning ability. Inspired by the way humans reason with mental imagery--the internal construction and manipulation of visual cues--we investigate whether VLMs can reason through interleaved multimodal trajectories without producing explicit images. To this end, we present a Machine Mental Imagery framework, dubbed as "Mirage", which augments VLM decoding with latent visual tokens alongside ordinary text. Concretely, whenever the model chooses to "think visually", it recasts its hidden states as next tokens, thereby continuing a multimodal trajectory without generating pixel-level images. Begin by supervising the latent tokens through distillation from ground-truth image embeddings, we then switch to text-only supervision to make the latent trajectory align tightly with the task objective. A subsequent reinforcement learning stage further enhances the multimodal reasoning capability. Experiments on diverse benchmarks demonstrate that \Model unlocks stronger multimodal reasoning without explicit image generation.
InTrain: Intrinsic Trainability for Zero-Cost Neural Architecture Search
Authors: Qinqin Zhou; Fuhai Chen; Jipeng Wu; Zhiwei Chen; Zhikai Hu; Weiwei Cai
Venue: CVPR 2026 (main)

Abstract: Training-free neural architecture search promises efficient discovery of high-performance networks without costly training. However, existing zero-cost proxies rely on fragmented heuristics that fail to capture the fundamental question: what makes an architecture trainable? This paper introduces Intrinsic Trainability (InTrain), a unified theoretical proxy that formalizes trainability as an architectural invariant emerging from two synergistic components: geometric capacity and optimization resilience. We operationalize intrinsic trainability through analysis of neural information processing. Geometric capacity is quantified via the participation ratio of activation covariance eigenspectrum, capturing the effective dimensionality of representation manifolds. Optimization resilience is measured through cumulative gradient health, assessing the robustness of backpropagation across network depth. InTrain synthesizes these dimensions through a scale-invariant multiplicative coupling, which we validate is essential for capturing their synergistic, non-additive relationship. Extensive experiments on standard NAS benchmarks and search spaces demonstrate that InTrain achieves ranking correlations on par with state-of-the-art ensemble-based proxies and outperforms other single-metric methods.
Flow Map Distillation Without Data
Authors: Shangyuan Tong; Nanye Ma; Saining Xie; Tommi Jaakkola
Venue: CVPR 2026 (main)

Abstract: State-of-the-art flow models achieve remarkable quality but require slow, iterative sampling. To accelerate this, flow maps can be distilled from pre-trained teachers, a procedure that conventionally requires sampling from an external dataset. We argue that this data-dependency introduces a fundamental risk of Teacher-Data Mismatch, as a static dataset may provide an incomplete or even misaligned representation of the teacher's full generative capabilities. This leads us to question whether this reliance on data is truly necessary for successful flow map distillation. In this work, we explore a data-free alternative that samples only from the prior distribution--a distribution the teacher is guaranteed to follow by construction, thereby circumventing the mismatch risk entirely. To demonstrate the practical viability of this philosophy, we introduce a principled framework that learns to predict the teacher's sampling path while actively correcting for its own compounding errors to ensure high fidelity. Our approach surpasses all data-based counterparts and establishes a new state-of-the-art by a significant margin. Specifically, distilling from SiT-XL/2+REPA, our method reaches an impressive FID of 1.45 on ImageNet 256x256, and 1.49 on ImageNet 512x512, both with only 1 sampling step. We hope our work establishes a more robust paradigm for accelerating generative models and motivates the broader adoption of flow map distillation without data.
Unified Vector Floorplan Generation via Markup Representation
Authors: Kaede Shiohara; Toshihiko Yamasaki
Venue: CVPR 2026 (main)

Abstract: Automatic residential floorplan generation has long been a central challenge bridging architecture and computer graphics, aiming to make spatial design more efficient and accessible. While early methods based on constraint satisfaction or combinatorial optimization ensure feasibility, they lack diversity and flexibility. Recent generative models achieve promising results but struggle to generalize across heterogeneous conditional tasks, such as generation from site boundaries, room adjacency graphs, or partial layouts, due to their suboptimal representations. To address this gap, we introduce Floorplan Markup Language (FML), a general representation that encodes floorplan information within a single structured grammar, which casts the entire floorplan generation problem into a next token prediction task. Leveraging FML, we develop a transformer-based generative model, Floorplan Markup Language Model (FMLM), capable of producing high-fidelity and functional floorplans under diverse conditions. Comprehensive experiments on the RPLAN dataset demonstrate that FMLM, despite being a single model, surpasses the previous task-specific state-of-the-art methods.
Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification
Authors: Bo Zhang; Xinan Xu; Shuo Yan; Yu Bai; Zheng Zhang; Wufan Wang; Hui Gao; Wendong Wang
Venue: CVPR 2026 (main)

Abstract: Recent pseudo-bag augmentation methods for Multiple Instance Learning (MIL)-based Whole Slide Image (WSI) classification sample instances from a limited number of bags, resulting in constrained diversity. To address this issue, we propose Contrastive Cross-Bag Augmentation (C2Aug) to sample instances from all bags with the same class to increase the diversity of pseudo-bags. However, introducing new instances into the pseudo-bag increases the number of critical instances (e.g., tumor instances). This increase results in a reduced occurrence of pseudo-bags containing few critical instances, thereby limiting model performance, particularly on test slides with small tumor areas. To address this, we introduce a bag-level and group-level contrastive learning framework to enhance the discrimination of features with distinct semantic meanings, thereby improving model performance. C2Aug samples instances with consistent bag-level labels across the entire dataset, which simultaneously enhances the diversity of pseudo-bags and mitigates label noise. Experimental results demonstrate that C2Aug consistently outperforms state-of-the-art approaches across multiple evaluation metrics. Our code is publicly available at: https://github.com/weiaicunzai/mixup.