GenAI4ELab/papercli-papers huggingface dataset:
MHopReg: Efficient Hierarchical Multi-Hop Graph Search for Point Cloud Registration
Authors: Yue Wu; Feng Xiao; Yongzhe Yuan; Hao Li; Kaiyuan Feng; Maoguo Gong; Qiguang Miao; Wenping Ma
Venue: CVPR 2026 (main)

Abstract: Outlier rejection for correspondence-based point cloud registration confronts two fundamental challenges in real-world scenarios. First, low-overlap regions yield sparse and fragmented inlier distributions that are difficult to discover using conventional one-step global search strategies. Second, large-scale scenes present dense correspondence inputs that impose stringent requirements on the accuracy-efficiency trade-off of search algorithms. To this end, we propose a hierarchical multi-hop graph search framework that progressively refines correspondences to address these challenges. Our method constructs a compatibility graph with transformation-invariant embeddings to predict correspondence confidence, establishing the foundation for cluster-balanced seed sampling that ensures comprehensive coverage across fragmented regions. These strategically selected seeds subsequently drive hierarchical multi-hop expansion, progressively discovering inliers through multi-resolution graph layers while circumventing the high complexity of exhaustive global search. Finally, distribution-aware ranking jointly evaluates geometric consistency and spatial coverage to select well-distributed transformations from multiple hypotheses. Experiments on 3DMatch, 3DLoMatch, and KITTI demonstrate that our method achieves highly competitive performance compared to existing methods in both low-overlap and large-scale scenarios.
PPISP: Physically-Plausible Compensation and Control of Photometric Variations in Radiance Field Reconstruction
Authors: Isaac Deutsch; Nicolas Moënne-Loccoz; Gavriel State; Zan Gojcic
Venue: CVPR 2026 (main)

Abstract: Multi-view 3D reconstruction methods remain highly sensitive to photometric inconsistencies arising from camera optical characteristics and variations in image signal processing (ISP). Existing mitigation strategies such as per-frame latent variables or affine color corrections lack physical grounding and generalize poorly to novel views. We propose the Physically-Plausible ISP (PPISP) correction module, which disentangles camera-intrinsic and capture-dependent effects through physically based and interpretable transformations. A dedicated PPISP controller, trained on the input views, predicts ISP parameters for novel viewpoints, analogous to auto exposure and auto white balance in real cameras. This design enables realistic and fair evaluation on novel views without access to ground-truth images. PPISP achieves SotA performance on standard benchmarks, while providing intuitive control and supporting the integration of metadata when available. The source code is available at: https://github.com/nv-tlabs/ppisp
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
Authors: Tianyu Yu; Zefan Wang; Chongyi Wang; Fuwei Huang; Wenshuo Ma; Zhihui He; Tianchi Cai; Weize Chen; Yuxiang Huang; Ranchi Zhao; Bokai Xu; Junbo Cui; Yingjing Xu; Liqing Ruan; Luoyuan Zhang; Hanyu Liu; Jingkun Tang; Hongyuan Liu; Qining Guo; Wenhao Hu; Bingxiang He; Jie Zhou; Jie Cai; Ji Qi; Zonghao Guo; Chi Chen; Guoyang Zeng; Yuxuan Li; Ganqu Cui; Ning Ding; Xu Han; Yuan Yao; Zhiyuan Liu; Maosong Sun
Venue: CVPR 2026 (main)

Abstract: Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged as a core bottleneck in making MLLMs more accessible and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B parameter model designed for high efficiency and strong performance. We introduce three core improvements in model architecture, data strategy and training method: a unified 3D-Resampler model architecture for highly compact encoding over images and videos, a unified learning paradigm for document knowledge and text recognition without heavy data engineering, and a hybrid reinforcement learning strategy for proficiency in both short and long reasoning modes. Comprehensive experimental results in OpenCompass evaluation show that MiniCPM-V 4.5 surpasses widely used proprietary models such as GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL 72B. Notably, the strong performance is achieved with remarkable efficiency. For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 surpasses Qwen2.5-VL 7B by +2.8, using just 46.7% GPU memory and 8.7% inference time.
EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation
Authors: Tianwei Xiong; Jun Hao Liew; Zilong Huang; Zhijie Lin; Jiashi Feng; Xihui Liu
Venue: CVPR 2026 (main)

Abstract: Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce **EVATok**, a framework to produce **E**fficient **V**ideo **A**daptive **Tok**enizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenizers that encode videos based on the assignments predicted by routers. We demonstrate that EVATok delivers substantial improvements in efficiency and overall quality for video reconstruction and downstream AR generation. Enhanced by our advanced training recipe that integrates video semantic encoders, EVATok achieves superior reconstruction and state-of-the-art class-to-video generation on UCF-101, with at least 24.4% savings in average token usage compared to the prior state-of-the-art LARP and our fixed-length baseline.
End-to-End Language-Action Model for Humanoid Whole Body Control
Authors: Yuxuan Wang; Haobin Jiang; Shiqing Yao; Ziluo Ding; Zongqing Lu
Venue: CVPR 2026 (main)

Abstract: Existing humanoid control systems often rely on teleoperation or modular generation pipelines that separate language understanding from physical execution. However, the former is entirely human-driven, and the latter lacks tight alignment between language commands and physical behaviors. In this paper, we present SENTINEL, a fully end-to-end language-action framework for humanoid whole-body control. We construct a large-scale dataset by tracking human motions in simulation using a pretrained whole body controller, combined with their text annotations. The model directly maps language commands and proprioceptive inputs to low-level actions without any intermediate representation. The model generates action chunks using flow matching, which can be subsequently refined by a residual action head for real-world deployment. Our method exhibits strong semantic understanding and stable execution on humanoid robots in both simulation and real-world deployment, and also supports multi-modal extensions by converting inputs into texts.
Self-Diffusion Driven Blind Imaging
Authors: Yanlong Yang; Guanxiong Luo
Venue: CVPR 2026 (main)

Abstract: Optical imaging systems are inherently imperfect due to diffraction limits, lens manufacturing tolerances, assembly misalignment, and other physical constraints. In addition, unavoidable camera shake and object motion further introduce non-ideal degradations during acquisition. These aberrations and motion-induced variations are typically unknown, difficult to measure, and costly to model or calibrate in practice. Blind inverse problems offer a promising direction by jointly estimating both the latent image and the unknown degradation kernel. However, existing approaches often suffer from convergence instability, limited prior expressiveness, and sensitivity to hyperparameters. Inspired by recent advances in self-diffusion, we propose DeblurSDI, a zero-shot, self-supervised blind imaging framework that requires no pre-training. DeblurSDI formulates blind image recovery as an iterative reverse self-diffusion process that begins from pure noise and progressively refines both the sharp image and the blur kernel. Extensive experiments on combined optical aberrations and motion blur demonstrate that DeblurSDI consistently outperforms other methods by a substantial margin.
Delta Rectified Flow Sampling for Text-to-Image Editing
Authors: Gaspard Beaudouin; Minghan Li; Jaeyeon Kim; Sung-Hoon Yoon; Mengyu Wang
Venue: CVPR 2026 (main)

Abstract: We propose Delta Rectified Flow Sampling (DRFS), a novel inversion-free, path-aware editing framework within rectified flow models for text-to-image editing. DRFS is a distillation-based method that explicitly models the discrepancy between the source and target velocity fields in order to mitigate over-smoothing artifacts rampant in prior distillation sampling approaches. We further introduce a time-dependent shift term to push noisy latents closer to the target trajectory, enhancing the alignment with the target distribution. We theoretically demonstrate that disabling this shift recovers Delta Denoising Score (DDS), bridging score-based diffusion optimization and velocity-based rectified-flow optimization. Moreover, under rectified-flow dynamics, a linear shift schedule recovers the inversion-free method FlowEdit as a strict special case, yielding a unifying view of optimization and ODE editing. We conduct an analysis to guide the design of our shift term, and experimental results on the widely used PIE Benchmark indicate that DRFS achieves superior editing quality, fidelity, and controllability while requiring no architectural modifications. Code is available at https://github.com/Harvard-AI-and-Robotics-Lab/DeltaRectifiedFlowSampling.
Multi-level Causal LLM-based Text-to-Motion Generation with Human Alignment
Authors: Xiaodong Chen; Qian Bao; Xudong Liu; Jianping Fang; Jintao Fang; Yongdong Zhang; Tao Mei; Wu Liu
Venue: CVPR 2026 (main)

Abstract: Although progress has been made in LLM-based text-driven motion generation, it still has the limitations of generating fine-grained and semantically consistent motions. These limitations stem from: 1) fine-grained motion quantization errors; 2) mismatches between causal reasoning language and non-causal motion representation; and 3) lack of human preference alignment. To solve them, this paper proposes MoTiGA, a multi-level causal LLM-based text-to-motion generation framework with human alignment. Firstly, MoTiGA employs Causal RVQ-VAE for multi-level causal fine-grained motion representation, then explores iterative residual quantization and causal convolutions to reduce fine-grained motion quantization errors, while preserving the causality as language presentation. Furthermore, the framework incorporates a time-lagged causal prediction strategy, enabling parallel prediction across motion token levels while maintaining temporal dependencies. Finally, to enhance human alignment, we propose Multi-level Hybrid-weighted Preference Optimization (MHPO), which dynamically adjusts semantic similarity weighting and continuous similarity scores. For MHPO, we also release the HumanML3D-R dataset, the first large-scale preference dataset for motion generation, with 101,490 human preference pairs. Evaluations show MoTiGA's superior performance, with an 82.3% FID improvement on HumanML3D and a 64.7% improvement on KIT-ML over other LLM-based methods.
EgoProx: Evaluating MLLMs on Egocentric 3D Proximity Reasoning Across a Cognitive Hierarchy
Authors: Jinzhao Li; Yinuo Chen; Dongxu Piao; Panwang Pan; Yifan Yu; Dong Wang; Honglei Yan; Liang Yue; Shaofei Wang; Yixin Chen; Siyuan Huang; Miao Liu
Venue: CVPR 2026 (main)

Abstract: Humans constantly reason about 3D proximity, the relations between their body and surrounding objects, to guide perception and action in daily life. Whether multimodal large language models (MLLMs) can perform such embodied 3D reasoning remains unclear. To this end, we introduce EgoProx, a benchmark for egocentric 3D proximity reasoning. We organize our tasks along a cognitive chain, covering intention, exploration, exploitation, and chain-of-actions reasoning. We also design an agent based data engine that produces diverse and consistent QA pairs at scale. We benchmark prevailing MLLMs on EgoProx and conduct additional analyses with dataset specific and task specific instruction tuning. We observe large cross-domain gains, indicating that current MLLMs contain some spatial knowledge; however, they still struggle to effectively leverage it for spatial reasoning VQA.
IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting
Authors: Wei Long; Haifeng Wu; Shiyin Jiang; Jinhua Zhang; Xinchun Ji; Shuhang Gu
Venue: CVPR 2026 (main)

Abstract: Generalizable 3D Gaussian Splatting aims to directly predict Gaussian parameters using a feed-forward network for scene reconstruction. Among these parameters, Gaussian means are particularly difficult to predict, so depth is usually estimated first and then unprojected to obtain the Gaussian sphere centers. Existing methods typically rely solely on a single warp to estimate depth probability, which hinders their ability to fully leverage cross-view geometric cues, resulting in unreliable and coarse depth maps. To address this limitation, we propose IDESplat, which iteratively applies warp operations to boost depth probability estimation for accurate Gaussian mean prediction. First, to eliminate the inherent unreliability of a single warp, we introduce a Depth Probability Boosting Unit (DPBU) that integrates epipolar attention maps produced by cascading warp operations in a multiplicative manner. Next, we construct an iterative depth estimation process by stacking multiple DPBUs, progressively identifying potential depth candidates with high likelihood. As IDESplat iteratively boosts depth probability estimates and updates the depth candidates, the depth map is gradually refined, resulting in accurate Gaussian means. We conduct experiments on RealEstate10K, ACID and DL3DV. IDESplat achieves outstanding reconstruction quality and state-of-the-art performance with real-time efficiency. On RE10K, it outperforms DepthSplat by 0.33 dB in PSNR, using only 10.7% of the parameters and 70% of the memory. Additionally, our IDESplat improves PSNR by 2.95 dB over DepthSplat on the DTU dataset in cross-dataset experiments, demonstrating its strong generalization ability.
Reliable Policy Transfer for Safety-Aware End-to-End Driving with Deep Reinforcement Learning
Authors: Uddin Md. Borhan; Arif Raza; Zhiliang Lin; Lu Wang; Jianqiang Li; Jie Chen
Venue: CVPR 2026 (main)

Abstract: End-to-End (E2E) Reinforcement Learning (RL) for autonomous driving still struggles with safety and generalization under distribution shift, as perception-heavy encoders, sparse rewards, and ad hoc uncertainty handling yield brittle closed-loop behavior. This work introduces a unified Deep RL (DRL) framework built around a control-layer reliability interface where the same uncertainty signal informs relational attention, gates policy entropy, and regularizes transfer alignment. An ego-centric relational graph encodes agent influence via uncertainty-weighted attention over kinematics, lane geometry, and semantics, producing a compact control state. A multi-objective differentiable reward shapes safety, progress, and comfort with an uncertainty term. Aleatoric and epistemic uncertainty, captured through per-edge heteroscedastic variance and a critic ensemble, modulate policy entropy for risk-aware exploration. A causal-semantic transfer objective aligns actions, attention, and uncertainty statistics across domains with meta-learned initialization for few-shot adaptation. In closed-loop urban driving across varied towns, traffic, and weather, the framework improves success rate, reduces infractions, and achieves higher time-to-conflict combined with lower lateral deviation over strong baselines.
LacTokGen: Latent Consistency Tokenizer for 1024-pixel Image Generation by 256 Tokens
Authors: Qingsong Xie; Luyuan Zhang; Zhao Zhang; Siyuan Li; Zhe Huang; Zhenyu Yang; Haonan Lu
Venue: CVPR 2026 (main)

Abstract: Image tokenization has significantly advanced visual generation and multimodal modeling, particularly when paired with autoregressive models. However, current methods face challenges in balancing efficiency and quality: high-resolution image generation either requires an excessive number of tokens or compromises critical details through token reduction. To resolve this, we propose Latent Consistency Tokenizer (LacTok) that bridges discrete visual tokens with the compact latent space of pretrained latent diffusion models (LDMs), enabling efficient representation of 1024x1024 images using only 256 tokens--a 16xcompression over VQGAN. LacTok integrates a transformer encoder, a quantized codebook, and a latent consistency decoder. Direct application of LDM as the decoder results in color and brightness discrepancies; thus, we convert it to latent consistency decoder, reducing multi-step sampling to 1-2 steps for direct pixel-level supervision. To endow LacTok with text-to-image  generation capabilities, we seamlessly integrate it with an autoregressive transformer, forming LacTokGen. This transformer is trained by predicting compact token sequences conditioned on text instructions. Experiments demonstrate LacTok's superiority in high-fidelity reconstruction, with 10.8 reconstruction Frechet Inception Distance on MSCOCO-2017 5K benchmark for 1024x1024 image reconstruction. LacTokGen achieves 0.73 score on GenEval benchmark and 0.304 HPSv2 on MSCOCO-2017 dataset.
ForeHOI: Feed-forward 3D Object Reconstruction from Daily Hand-Object Interaction Videos
Authors: Yuantao Chen; Jiahao Chang; Chongjie Ye; Chaoran Zhang; Zhaojie Fang; Chenghong Li; Xiaoguang Han
Venue: CVPR 2026 (main)

Abstract: The ubiquity of monocular videos capturing daily hand-object interactions presents a valuable resource for embodied intelligence. While 3D hand reconstruction from in-the-wild videos has seen significant progress, reconstructing the involved objects remains challenging due to severe occlusions and the complex, coupled motion of the camera, hands, and object. In this paper, we introduce ForeHOI, a novel feed-forward model that directly reconstructs 3D object geometry from monocular hand-object interaction videos within one minute of inference time, eliminating the need for any pre-processing steps. Our key insight is that, the joint prediction of 2D mask inpainting and 3D shape completion in a feed-forward framework can effectively address the problem of severe occlusion in monocular hand-held object videos, thereby achieving results that outperform the performance of optimization-based methods. The information exchanges between the 2D and 3D shape completion boosts the overall reconstruction quality, enabling the framework to effectively handle severe hand-object occlusion. Furthermore, to support the training of our model, we contribute the first large-scale, high-fidelity synthetic dataset of hand-object interactions with comprehensive annotations. Extensive experiments demonstrate that ForeHOI achieves state-of-the-art performance in object reconstruction, significantly outperforming previous methods with around a 100x speedup. Code and data are available at: https://github.com/Tao-11-chen/ForeHOI.
BEV-SLD: Self-Supervised Scene Landmark Detection for Global Localization with LiDAR Bird's-Eye View Images
Authors: David Skuddis; Vincent Ress; Wei Zhang; Vincent Ofosu Nyako; Norbert Haala
Venue: CVPR 2026 (main)

Abstract: We present BEV-SLD, a LiDAR global localization method building on the Scene Landmark Detection (SLD) concept. Unlike scene-agnostic pipelines, our self-supervised approach leverages bird's-eye-view (BEV) images to discover scene-specific patterns at a prescribed spatial density and treat them as landmarks. A consistency loss aligns learnable global landmark coordinates with per-frame heatmaps, yielding consistent landmark detections across the scene. Across campus, industrial, and forest environments, BEV-SLD delivers robust localization and achieves strong performance compared to state-of-the-art methods.
Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision
Authors: Yunhe Gao; Yabin Zhang; Chong Wang; Jiaming Liu; Maya Varma; Jean-Benoit Delbrouck; Akshay Chaudhari; Curtis Langlotz
Venue: CVPR 2026 (main)

Abstract: Foundation models have transformed vision and language by learning general-purpose representations from large-scale unlabeled data, yet 3D medical imaging lacks analogous approaches. Existing self-supervised methods rely on low-level reconstruction or contrastive objectives that fail to capture the anatomical semantics critical for medical image analysis, limiting transfer to downstream tasks. We present MASS (MAsk-guided Self-Supervised learning), which treats in-context segmentation as the pretext task for learning general-purpose medical imaging representations. MASS's key insight is that automatically generated class-agnostic masks provide sufficient structural supervision for learning semantically rich representations. By training on thousands of diverse mask proposals spanning anatomical structures and pathological findings, MASS learns what semantically defines medical structures: the holistic combination of appearance, shape, spatial context, and anatomical relationships. We demonstrate effectiveness across data regimes: from small-scale pretraining on individual datasets (20-200 scans) to large-scale multi-modal pretraining on 5K CT, MRI, and PET volumes, all without annotations. MASS demonstrates: (i) few-shot segmentation on novel structures, (ii) matching full supervision with only 20-40% labeled data while outperforming self-supervised baselines by over 20 in Dice score in low-data regimes, and (iii) frozen-encoder classification on unseen pathologies that matches full supervised training with thousands of samples. Mask-guided self-supervised pretraining captures broadly generalizable knowledge, opening a path toward 3D medical imaging foundation models without expert annotations. Code is available \href https://github.com/Stanford-AIMI/MASS  here .
ST4R-Splat: Spatio-Temporal Referring Segmentation in 4D Gaussian Splatting
Authors: Yuming Meng; Dong Wu; Hongbin Zha
Venue: CVPR 2026 (main)

Abstract: Understanding objects in dynamic 4D environments via natural language is crucial yet underexplored. While existing methods focus on static 3D referring segmentation or open-vocabulary 4D querying, they struggle to ground complex spatio-temporal referring expressions in explicit 4D reconstructions. We introduce Spatio-Temporal Referring Segmentation in 4D Gaussian Splatting (STRS-4DGS), a novel task aiming to jointly identify and segment a target instance across space and time given a referring expression. To tackle this, we propose ST4R-Splat, the first framework for STRS-4DGS. Specifically, our framework incorporates an Instance-Aware 4D Gaussian Referring Field that assigns time-invariant embeddings for robust spatial grounding, and an Instance-Level Temporal State Mapping module that enables view-independent temporal localization directly in feature space. To provide rich spatio-temporal semantic supervision, we develop an automatic, MLLM-based captioning pipeline that generates decoupled spatial and temporal textual descriptions. Evaluated on our newly constructed STRS-4DGS benchmark, our method significantly outperforms adapted state-of-the-art baselines across both time-agnostic and time-sensitive metrics, establishing a strong foundation for language-driven 4D scene understanding.