GenAI4ELab/papercli-papers huggingface dataset:
Language-Grounded Decoupled Action Representation for Robotic Manipulation
Authors: Wuding Weng; Tongshu Wu; Liucheng Chen; Siyu Xie; Zheng Wang; Xing Xu; Jingkuan Song; Heng Tao Shen
Venue: CVPR 2026 (main)

Abstract: The heterogeneity between high-level vision-language understanding and low-level action control remains a fundamental challenge in robotic manipulation. Although recent methods have advanced task-specific action alignment, they often struggle to generate robust and accurate actions for novel or semantically related tasks. To address this, we propose the Language-Grounded Decoupled Action Representation (LaDA) framework, which leverages natural language as a semantic bridge to connect perception and control. LaDA introduces a fine-grained intermediate layer of three interpretable action primitives--translation, rotation, and gripper control--providing explicit semantic structure for low-level actions. It further employs a semantic-guided soft-label contrastive learning objective to align similar action primitives across tasks, enhancing generalization and motion consistency. An adaptive weighting strategy, inspired by curriculum learning, dynamically balances contrastive and imitation objectives for stable and effective training. Extensive experiments on simulated benchmarks (LIBERO and MimicGen) and real-world demonstrations validate that LaDA achieves strong performance and generalizes effectively to unseen or related tasks.
Parameterized Prompt for Incremental Object Detection
Authors: Zijia An; Boyu Diao; Ruiqi Liu; Libo Huang; Chuanguang Yang; Fei Wang; Zhulin An; Yongjun Xu
Venue: CVPR 2026 (main)

Abstract: Recent studies have demonstrated that incorporating trainable prompts into pretrained models enables effective incremental learning. However, the application of prompts in incremental object detection (IOD) remains underexplored. Our study reveals that existing prompts-pool-based approaches assume disjoint class sets across incremental tasks, which are unsuitable for IOD as they overlook the inherent co-occurrence phenomenon in detection. In co-occurring scenarios, unlabeled objects from previous tasks may appear in current task images, leading to confusion in prompts pool. In this paper, we hold that prompt structures should exhibit adaptive consolidation properties across tasks, with constrained updates to prevent confusion and catastrophic forgetting. Motivated by this, we introduce Parameterized Prompts for Incremental Object Detection (P^2IOD). Leveraging neural networks global evolution properties, P^2IOD employs networks as the parameterized prompts to adaptively consolidate knowledge across tasks. To constrain prompts structure updates, P^2IOD further engages a parameterized prompts fusion strategy. Extensive experiments on PASCAL VOC2007 and MS COCO datasets demonstrate that P^2IOD's effectiveness in IOD and achieves the state-of-the-art performance among existing baselines.
PixelDiT: Pixel Diffusion Transformers for Image Generation
Authors: Yongsheng Yu; Wei Xiong; Weili Nie; Yichen Sheng; Shiqiu Liu; Jiebo Luo
Venue: CVPR 2026 (main)

Abstract: Latent-space modeling has been the standard for Diffusion Transformers (DiTs). However, it relies on a two-stage pipeline where the pretrained autoencoder introduces lossy reconstruction, leading to error accumulation while hindering joint optimization. To address these issues, we propose PixelDiT, a single-stage, end-to-end model that eliminates the need for the autoencoder and learns the diffusion process directly in the pixel space. PixelDiT adopts a fully transformer-based architecture shaped by a dual-level design: a patch-level DiT that captures global semantics and a pixel-level DiT that refines texture details, enabling efficient training of a pixel-space diffusion model while preserving fine details. PixelDiT achieves 1.61 FID on ImageNet 256 and 1.81 FID on ImageNet 512, surpassing existing pixel generative models by a large margin. We further extend PixelDiT to text-to-image generation and pretrain it at the 1024^ 2  resolution in pixel space. It achieves 0.74 on GenEval and 83.5 on DPG-bench, approaching the best latent diffusion models.
Video-CoE: Reinforcing Video Event Prediction via Chain of Events
Authors: Qile Su; Jing Tang; Rui Chen; Lei Sun; Xiangxiang Chu
Venue: CVPR 2026 (main)

Abstract: Despite advances in the application of MLLMs for various video tasks, video event prediction (VEP) remains relatively underexplored. VEP requires the model to perform fine-grained temporal modeling of videos and establish logical relationships between videos and future events, which current MLLMs still struggle with. In this work, we first present a comprehensive evaluation of current leading MLLMs on the VEP task, revealing the reasons behind their inaccurate predictions, including lack of logical reasoning ability for future events prediction and insufficient utilization of visual information. To address these challenges, we propose **C**hain **o**f **E**vents (**CoE**) paradigm, which constructs temporal event chains to implicitly enforce MLLM focusing on the visual content and the logical connections between videos and future events, incentivizing model's reasoning capability with multiple training protocols. Experimental results on public benchmarks demonstrate that our method outperforms both leading open-source and commercial MLLMs, establishing a new state-of-the-art on the VEP task.
FiDeSR: High-Fidelity and Detail-Preserving One-Step Diffusion Super-Resolution
Authors: Aro Kim; Myeongjin Jang; Chaewon Moon; Youngjin Shin; Jinwoo Jeong; Sang-hyo Park
Venue: CVPR 2026 (main)

Abstract: Diffusion-based approaches have recently driven remarkable progress in real-world image super-resolution (SR). However, existing methods still struggle to simultaneously preserve fine details and ensure high-fidelity reconstruction, often resulting in suboptimal visual quality. In this paper, we propose FiDeSR, a high-fidelity and detail-preserving one-step diffusion super-resolution framework. During training, we introduce a detail-aware weighting strategy that adaptively emphasizes regions where the model exhibits higher prediction errors. During inference, low- and high-frequency adaptive enhancers further refine the reconstruction without requiring model retraining, enabling flexible enhancement control. To further improve the reconstruction accuracy, FiDeSR incorporates a latent residual refinement, which corrects prediction errors in the diffusion noise and enhances fine detail recovery. FiDeSR achieves superior real-world SR performance compared to existing diffusion-based methods, producing outputs with both high perceptual quality and faithful content restoration. The source code will be released at: https://github.com/Ar0Kim/FiDeSR.
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Authors: Jingxuan Zhang; Yunta Hsieh; Zhongwei Wan; Haokun Lin; Xin Wang; Ziqi Wang; Yingtie Lei; Mi Zhang
Venue: CVPR 2026 (main)

Abstract: Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing compute and memory demands, especially as models scale to longer horizons and larger backbones. To address these bottlenecks, we introduce QuantVLA, a training-free post-training quantization (PTQ) framework that, to our knowledge, is the first PTQ approach for VLA systems and the first to successfully quantize a diffusion transformer (DiT) action head. QuantVLA incorporates three scale-calibrated components: (1) a selective quantization layout that integerizes all linear layers in both the language backbone and the DiT while keeping attention projections in floating point to preserve the original operator schedule; (2) attention temperature matching, a lightweight per-head scaling mechanism that stabilizes attention logits and is folded into the dequantization scales at inference; and (3) output head balancing, a per-layer residual interface calibration that mitigates post-projection energy drift. The framework requires no additional training, uses only a small unlabeled calibration buffer, and supports integer kernels for low-bit weights and activations while leaving the architecture unchanged. Across representative VLA models on LIBERO, QuantVLA exceeds the task success rates of full-precision baselines, achieves about 70% relative memory savings on the quantized components, providing a practical pathway toward scalable low-bit embodied intelligence under strict compute, memory, and power constraints.
BrepGaussian: CAD reconstruction from Multi-View Images with Gaussian Splatting
Authors: Jiaxing Yu; Dongyang Ren; Hangyu Xu; Zhouyuxiao Yang; Yuanqi Li; Jie Guo; Zhengkang Zhou; Yanwen Guo
Venue: CVPR 2026 (main)

Abstract: The boundary representation (B-Rep) models a 3D solid as its explicit boundaries: trimmed corners, edges, and faces. Recovering B-Rep representation from unstructured data is a challenging and valuable task of computer vision and graphics. Recent advances in deep learning have greatly improved the recovery of 3D shape geometry, but still depend on dense and clean point clouds and struggle to generalize to novel shapes. We propose B-Rep Gaussian Splatting (BrepGaussian), a novel framework that learns 3D parametric representations from 2D images. We employ a Gaussian Splatting renderer with learnable features, followed by a specific fitting strategy. To disentangle geometry reconstruction and feature learning, we introduce a two-stage learning framework that first captures geometry and edges and then refines patch features to achieve clean geometry and coherent instance representations. Extensive experiments demonstrate the superior performance of our approach to state-of-the-art methods.
HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models
Authors: Minghui Lin; Pengxiang Ding; Shu Wang; Zifeng Zhuang; Yang Liu; Xinyang Tong; Wenxuan Song; Shangke Lyu; Siteng Huang; Donglin Wang
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most VLAs assume the Markov property, relying only on the current observation and thus suffering from temporal myopia that degrades long-horizon coherence. In this work, we view motion as a more compact and informative representation of temporal context and world dynamics, capturing inter-state changes while filtering static pixel-level noise. From this perspective, HiF-VLA equips a motion-centric world model for the VLA, enabling agents to reason about temporal dynamics for future evolution during action generation. Building on this idea, we propose HiF-VLA (Hindsight, Insight, and Foresight for VLAs), a unified framework that leverages motion for bidirectional temporal reasoning. HiF-VLA encodes past dynamics through hindsight priors, anticipates future motion via foresight reasoning, and integrates both through a hindsight-modulated joint expert to enable a "think-while-acting" paradigm for long-horizon manipulation. As a result, HiF-VLA surpasses strong baselines on LIBERO-Long and CALVIN ABC-D benchmarks, while incurring negligible additional inference latency. Furthermore, HiF-VLA achieves substantial improvements in real-world long-horizon manipulation tasks, demonstrating its broad effectiveness in practical robotic settings.
VLM4RSDet: Collaborative Optimization with Vision-Language Model for Enhancing Remote Sensing Object Detection
Authors: Shuohao Shi; Qiang Fang; Xin Xu
Venue: CVPR 2026 (main)

Abstract: Closed-set object detection in remote sensing imagery has made significant progress, but achieving high detection accuracy remains challenging. Vision-Language Models (VLMs), which possess rich prior knowledge, offer a promising solution to this challenge. However, most existing VLMs are designed for open-vocabulary tasks and exhibit inherent limitations when directly applied to closed-set scenarios, such as notable accuracy degradation and high deployment costs. To address these issues, we propose VLM4RSDet, a novel collaborative training framework that leverages vision-language model to enhance the performance of conventional closed-set remote sensing object detectors. Notably, during inference, VLM4RSDet only retains the standard object detection architecture, thus avoiding any additional deployment overhead. Furthermore, we introduce a Global-Local Cross-Attention (GLCA) module and a Learnable Hierarchical Prediction Strategy (LHPS) to further improve collaborative training performance. Extensive experiments on five benchmark datasets demonstrate the effectiveness and robustness of our approach. In particular, our method outperforms the state-of-the-art by 7.5% in mAP_ 0.5:0.95  on the VisDrone2019 dataset. Our code is available at \href https://github.com/cszzshi/VLM4RSDet  https://github.com/cszzshi/VLM4RSDet .
Exact-GS: Mathematically Rigorous and Accurate 3D Gaussian Splatting for 3D X-ray Reconstruction
Authors: Guangpu Yang; Steffen Kieß; Hanxiang Luo; Xingyu Liu; Sven Simon
Venue: CVPR 2026 (main)

Abstract: We propose Exact-GS, a novel mathematically rigorous and accurate 3D Gaussian Splatting model designed to perform 3D X-ray computed tomography (CT) reconstruction and novel view synthesis. Recently, 3D Gaussian Splatting achieved considerable progress at 3D representation. Unfortunately, due to the affine approximation of the projective transformation, previous 3DGS-based methods inevitably suffer from artifacts and projection inconsistencies. To address this problem, some ray tracing based methods perform integration along the ray across Gaussians. However, these methods are computationally inefficient on the forward and backward pass. We introduce a novel closed-form splatting solution for this problem with mathematically rigorous derivation. Our model is the first to achieve the same exact rendering quality as ray tracing based methods without any approximation under a splatting-based formulation, enabling fast CUDA-based hardware rasterization. Additionally, we present a precise Gaussian-tile intersection algorithm, enabling faster and efficient rendering. We demonstrate the performance gains by reconstruction and novel view synthesis through different synthetic and real-world datasets. Code is publicly available at: https://github.com/brucee1323/Exact-GS.
SDGS: Spatial Difference Guided Gaussian Splatting for Simultaneous Localization and 3D Reconstruction
Authors: Yijian Tian; Mingtao Ou; Zijian Pan; Xinglong Ji
Venue: CVPR 2026 (main)

Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as a powerful explicit 3D representation, enabling photorealistic and real-time novel view synthesis. However, most 3DGS pipelines still assume precomputed camera poses and offline optimization, which introduces latency and makes them brittle in fast-motion, real-world scenarios. Existing online 3DGS systems mostly fall into two camps: (1) hybrid systems that rely on a separate traditional SLAM system for camera poses and optimize Gaussians decoupled from tracking, increasing system complexity; and (2) purely Gaussian-based systems that estimate poses from dense photometric errors, requiring repeated rendering of a large number of Gaussians and thus incurring high computational cost. Moreover, current online methods are often sensitive to motion blur and high dynamic range scenes, limiting their applicability in practice.We address these limitations with a sparse, edge-guided online 3DGS framework. Our method represents the scene as an edge-aligned sparse Gaussian map and estimates 6-DoF camera poses by aligning rendered 3D edges with observed 2D edges using a distance transform based objective, yielding roughly 2x faster per-iteration pose optimization than existing Gaussian-based systems while recovering clear scene geometry. We further leverage a dual-channel hybrid pixel vision sensor that outputs blur-free, high-frame-rate spatial-difference edge signals alongside RGB images, and use these signals both for robust edge-based tracking and for a mutual supervision scheme that mitigates motion blur in dense 3D reconstruction. Our system maintains stable tracking and high-fidelity geometry under extremely high-speed motion, where existing RGB-only methods fail, while remaining compatible with standard RGB cameras and achieving competitive tracking accuracy.
Global Structure-from-Motion Meets Feedforward Reconstruction
Authors: Linfei Pan; Johannes Schönberger; Marc Pollefeys
Venue: CVPR 2026 (main)

Abstract: Structure-from-Motion -- the process of simultaneously estimating camera poses and 3D scene structure from a collection of images -- remains a central challenge in computer vision, with many open problems yet to be solved. Recent advances in feedforward 3D reconstruction have made significant strides in overcoming persistent failure cases of classical SfM methods, particularly in scenarios characterized by low texture, limited overlap, and symmetries. However, while feedforward approaches excel in these challenging conditions, they often face limitations regarding scalability, accuracy, or robustness, and typically fall short of classical methods in standard reconstruction settings. In this work, we systematically analyze these limitations and propose a new Structure-from-Motion pipeline by combining the respective strengths of classical and feedforward methods. Extensive experiments across multiple datasets show the benefits of our approach, achieving state-of-the-art results across a wide range of scenarios. We share our system as an open-source implementation at https://github.com/colmap/gluemap.
TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models
Authors: Zhiheng Liu; Weiming Ren; Haozhe Liu; Zijian Zhou; Shoufa Chen; Haonan Qiu; Xiaoke Huang; Zhaochong An; Fanny Yang; Aditya Patel; Viktar Atliha; Tony Ng; Xiao Han; Chuyan Zhu; Chenyang Zhang; Ding Liu; Juan-Manuel Perez-Rua; Sen He; Jürgen Schmidhuber; Wenhu Chen; Ping Luo; Wei Liu; Tao Xiang; Jonas Schult; Yuren Cong
Venue: CVPR 2026 (main)

Abstract: Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.
Compositional Transformation Reasoning for Composed Video Retrieval
Authors: Sihong Huang; Jiaxin Wu; Dongmei Jiang; Yi Cai; Yaowei Wang; Xiaoyong Wei
Venue: CVPR 2026 (main)

Abstract: Composed Video Retrieval aims to retrieve a target video given a reference video and a textual modification describing the desired change. The core challenge lies in modeling compositional multimodal transformations, i.e., how entities, actions, and scenes evolve across video and language modalities in response to fine-grained textual edits. Existing methods address this issue by training on large-scale video-text-video triplets or by generating dense textual descriptions to capture subtle visual differences. However, these supervised approaches often rely on noisy web-scale data and dataset-specific correspondences, leading to overfitting and limited generalization in diverse or fine-grained scenarios, while also failing to effectively model compositional and temporal transformations. We propose Multi-objective Reasoning (MoRe), a zero-shot framework based on MLLMs for multi-objective candidate selection and fine-grained transformation reasoning. Our method decomposes the compositional transformation into three complementary reasoning dimensions, i.e., entity, action, and scene, and performs pairwise candidate reasoning to explicitly capture semantic evolution over time. Furthermore, we introduce a recall-oriented multi-objective candidate selection module that identifies high-quality retrieval targets by jointly balancing visual, textual, and multimodal similarities before transformation reasoning. Experiments on EgoCVR and WebVid-CoVR demonstrate the effectiveness of our method over state-of-the-art approaches under the zero-shot setting, with R@1 improvements of +5.8 and +10.8, respectively.
ImmerIris: A Large-Scale Dataset and Benchmark for Off-Axis and Unconstrained Iris Recognition in Immersive Applications
Authors: Yuxi Mi; Qiuyang Yuan; Zhizhou Zhong; Xuan Zhao; Jiaogen Zhou; Fubao Zhu; Jihong Guan; Shuigeng Zhou
Venue: CVPR 2026 (main)

Abstract: Recently, iris recognition is regaining prominence in immersive applications such as extended reality as a means of seamless user identification. This application scenario introduces unique challenges compared to traditional iris recognition under controlled setups, as the ocular images are primarily captured off-axis and less constrained, causing perspective distortion, intra-subject variation, and quality degradation in iris textures. Datasets capturing these challenges remain limited. This paper fills this gap by presenting a large-scale iris dataset collected via head-mounted displays, termed ImmerIris. It contains 499,791 ocular images from 546 subjects, and is, to our knowledge, the largest public iris dataset to date and among the first dedicated to immersive applications. It is accompanied by a comprehensive set of evaluation protocols that benchmark recognition systems under various challenging conditions. This paper also draws attention to a shared obstacle of current recognition methods, the reliance on a pre-processing, normalization stage, which is fallible in off-axis and unconstrained setups. To this end, this paper further proposes a normalization-free paradigm that directly learns from minimally adjusted ocular images. Despite its simplicity, it outperforms normalization-based prior arts, indicating a promising direction for robust iris recognition.