GenAI4ELab/papercli-papers huggingface dataset:
Learning 3D Shape Fidelity Metric from Real-world Distortions
Authors: Xuelu Feng; Tianyu Luan; Zixin Zhu; Akshobhya Sharma; Phani Nuney; Junsong Yuan; Chunming Qiao
Venue: CVPR 2026 (main)

Abstract: 3D generation and reconstruction have become essential in many computer vision applications, where the reconstructed or generated 3D shapes need to appear realistic to human perception. However, traditional metrics like Chamfer Distance to compare two 3D shapes focus primarily on matching accuracy of the shape geometry and fail to capture perceptual fidelity in the shape. While frequency-based metrics attempt to analyze shape details in the spectral domain, they still do not fully encapsulate the complexity of human perception. To address this gap, we propose a human-aligned fidelity metric that leverages local shape connectivity through a local attention mechanism to capture rich, detailed shape information. We also introduce the two-branch Real Shape Fidelity (RSF) dataset, including a main subset and test-only subset. This dataset generates 3D mesh distortions using real-world reconstruction and generation methods and annotated by hundreds of human subjects. Our metric named Local-Connection-based Shape Evaluation (LoCaSE), utilizes a PointNet-based backbone combined with Low-Rank Adaptation (LoRA)-style pretraining and finetuning to reduce model bias, while maintaining translation, rotation, and scale invariance. Experiments demonstrate that our approach achieves superior alignment with human perception compared to previous metrics.
UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation
Authors: Haopeng Chen; Yihao Ai; Kabeen Kim; Robby T. Tan; Yixin Chen; Bo Wang
Venue: CVPR 2026 (main)

Abstract: Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-light datasets and the loss of visual information under poor illumination. Recent domain adaptation techniques attempt to utilize well-lit labels by augmenting well-lit images to mimic low-light conditions. But handcrafted augmentations oversimplify noise patterns, while learning-based methods often fail to preserve high-frequency low-light characteristics, producing unrealistic images that lead pose models to generalize poorly to real low-light scenes. Moreover, recent pose estimators rely on image cues through image-to-keypoint cross-attention, but these cues become unreliable under low-light conditions. To address these issues, we propose Unsupervised Domain Adaptation for Pose Estimation (UDAPose), a novel framework that synthesizes low-light images and dynamically fuses visual cues with pose priors for improved pose estimation. Specifically, our synthesis method incorporates a Direct-Current-based High-Pass Filter (DHF) and a Low-light Characteristics Injection Module (LCIM) to inject high-frequency details from input low-light images, overcoming rigidity or the detail loss in existing approaches. Furthermore, we introduce a Dynamic Control of Attention (DCA) module that adaptively balances image cues with learned pose priors in the Transformer architecture. Experiments show that UDAPose outperforms state-of-the-art methods, with notable AP gains of 10.1 (56.4%) on the ExLPose-test hard set (LL-H) and 7.4 (31.4%) in cross-dataset validation on EHPT-XC.
Hyperbolic Prototype Learning with Uncertainty-Aware Consistency for Continual Test-Time Segmentation
Authors: Siddhant Gole; Akash Pal; Amit More; S Divakar Bhat; Subhasis Chaudhuri; Biplab Banerjee
Venue: CVPR 2026 (main)

Abstract: Continual Test-Time Adaptation (CTTA) for semantic segmentation is vital for deploying vision models in dynamic environments with persistent domain shifts. Existing methods often degrade over time as self-supervised updates amplify early prediction errors. We attribute this fragility to a geometric limitation: Euclidean feature spaces, with polynomial volume growth, lead to distorted semantic representations and crowded, unstable decision boundaries. We propose HyperProtoSeg, a hyperbolic prototypical segmentation network that learns geometrically optimal class prototypes in the Poincare ball. Leveraging the exponential expansion of hyperbolic space, it enforces large and uniform inter-class margins with low distortion, yielding well separated and curvature-stable embeddings. For robust online adaptation, we introduce Hyperbolic Boundary Consistency Adaptation (HBCA), which partitions pixels by cross-view consistency into confident "core" and uncertain "boundary" sets. HBCA applies geodesic distance minimization for confident regions and a novel Hyperbolic Directional Consistency Loss for uncertain ones, preventing error amplification. Experiments on challenging synthetic- to-real benchmarks (Cityscapes - ACDC, IDD - IDD-AW, SHIFT) show that HyperProtoSeg + HBCA achieves an average improvement of (1.94%,4.02%,1.24%) over state-of-the-art CTTA methods under severe structural shifts.
Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
Authors: Yuto Shibata; Kashu Yamazaki; Lalit Jayanti; Yoshimitsu Aoki; Mariko Isogawa; Katerina Fragkiadaki
Venue: CVPR 2026 (main)

Abstract: Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking within physics engines (GMT) have enabled virtual characters and humanoid robots to reproduce a broad range of human motions, these behaviors are primarily limited to contact-less social interactions or isolated movements. Assistive scenarios, by contrast, require continuous awareness of a human partner and rapid adaptation to their evolving posture and dynamics. In this paper, we formulate the imitation of closely interacting, force-exchanging human-human motion sequences as a multi-agent reinforcement learning problem. We jointly train partner-aware policies for both the supporter (assistant) agent and the recipient agent in a physics simulator to track assistive motion references. To make this problem tractable, we introduce a partner policies initialization scheme that transfers priors from single-human motion-tracking controllers, greatly improving exploration. We further propose dynamic reference retargeting and contact-promoting reward, which adapt the assistant's reference motion to the recipient's real-time pose and encourage physically meaningful support. We show that \model   is the first method capable of successfully tracking assistive interaction motions on established benchmarks, demonstrating the benefits of a multi-agent RL formulation for physically grounded and socially aware humanoid control.
WHU-MARS: A Multispectral Aerial-Ground Benchmark Towards Any-Scenario Person Re-Identification
Authors: Yuxuan Zhao; Zhongao Zhou; Bin Yang; He Li; Jian Liang; Jun Chen; Bo Du; Mang Ye
Venue: CVPR 2026 (main)

Abstract: Recent person re-identification (ReID) leverages heterogeneous sensing with multiple modalities and viewpoints to improve robustness across diverse conditions. However, most approaches target predefined scenario pairs (e.g., visible-infrared or aerial-ground) and train separate task-specific models. In contrast, real-world systems often query against galleries that cover all scenarios, making such designs inefficient and complex to deploy. To bridge this gap, we introduce Any-Scenario ReID (AS-ReID): given a query from any (modality, viewpoint) scenario, a single model retrieves the same identity from a heterogeneous gallery spanning all scenarios. Progress toward AS-ReID is limited by two factors: (i) the lack of a real-world-aligned benchmark with broad scenario coverage, and (ii) the challenge of learning representations with strong intra-identity cohesion and inter-identity discrimination under diverse scenarios. To this end, we construct WHU-MARS, a multispectral aerial-ground benchmark with 2,337 identities and 434,620 images captured by RGB, near-infrared, and thermal infrared cameras on both ground and UAV platforms. WHU-MARS spans day-night, multiple seasons, and diverse weather, enabling AS-ReID as well as conventional ReID tasks. We further propose the Unified Alignment and Discrimination (UAD) framework. Progressive Center Alignment (ProCA) aggregates multi-view features into modality centers and then aligns them toward identity centers to reduce scenario bias. Global Prototype Discrimination (GPD) contrasts samples against global identity prototypes to enforce large-margin discrimination. Extensive experiments highlight the challenges of WHU-MARS and demonstrate the effectiveness of UAD on AS-ReID.
SAGE: Scalable Agentic 3D Scene Generation for Embodied AI
Authors: Hongchi Xia; Xuan Li; Zhaoshuo Li; Qianli Ma; Jiashu Xu; Ming-Yu Liu; Yin Cui; Tsung-Yi Lin; Wei-Chiu Ma; Shenlong Wang; Shuran Song; Fangyin Wei
Venue: CVPR 2026 (main)

Abstract: Real-world data collection for embodied agents remains costly and unsafe, calling for scalable, realistic, and simulator-ready 3D environments. However, existing scene-generation systems often rely on rule-based or task-specific pipelines, yielding artifacts and physically invalid scenes. We present SAGE, an agentic framework that, given a user-specified embodied task (e.g., "pick up a bowl and place it on the table"), understands the intent and automatically generates simulation-ready environments at scale. The agent couples multiple generators for layout and object composition with critics that evaluate semantic plausibility, visual realism, and physical stability. Through iterative reasoning and adaptive tool selection, it self-refines the scenes until meeting user intent and physical validity. The resulting environments are realistic, diverse, and directly deployable in modern simulators for policy training. Policies trained purely on this data exhibit clear scaling trends and generalize to unseen objects and layouts, demonstrating the promise of simulation-driven scaling for embodied AI. Code, demos, and the SAGE-10k dataset can be found on the project page: https://research.nvidia.com/labs/dir/sage/.
GVIS: Generative Vector Image Steganography
Authors: Zihao Xu; Dawei Xu; Zihan Li; Xixi Zheng; Chuan Zhang
Venue: CVPR 2026 (main)

Abstract: Vector images have attracted increasing attention in the field of information hiding in recent years due to their scalability and structural properties. However, existing steganographic methods for vector images often introduce noticeable modifications to the files themselves, resulting in potential security risks and limited embedding capacity. Motivated by recent advances in diffusion models and image generative steganography, we propose GVIS, a novel Generative Vector Image Steganography framework. GVIS deterministically generates raster images using diffusion models, which are subsequently vectorized into vector images. On the sender side, we design a lightweight overlap detection algorithm to identify cubic Bezier curve control points suitable for data embedding, which enables the secret information to be encoded into the coordinate parameters of these control points. Then, the receiver can use the pre-shared settings to reconstruct the generation process and accurate message extraction by difference. Extensive theoretical analysis and experimental results demonstrate that GVIS achieves high-capacity, high-accuracy, secure, and training-free steganography. To the best of our knowledge, this is the first attempt to apply generative models to vector image steganography.
Photo-Guided Tooth Segmentation on 3D Oral Scan Model
Authors: Shaojie Zhuang; Guangshun Wei; Jiangxin He; Yuanfeng Zhou
Venue: CVPR 2026 (main)

Abstract: Accurate 3D tooth segmentation is fundamental for digital dentistry, orthodontic analysis, and clinical simulation. Intraoral scan (IOS) models often suffer from incomplete or unreliable texture information, making it difficult to delineate fine boundaries between teeth and gingiva, while 2D intraoral images provide rich semantic and chromatic information that can complement 3D geometry. Thus, we propose a novel Photo-guided 3D Model Tooth Segmentation framework, PMTSeg, that enhances 3D tooth segmentation by integrating texture cues from intraoral photos. Our framework introduces three key components: a Camera Alignment Module (CAM) for accurate image-model registration, a Feature Filtering Gate (FFG) for adaptive multi-view feature selection, and a Consistent Feature Learning (CFL) mechanism for learning texture-geometry correspondence. Our method supports arbitrary numbers and views of intraoral photos. Experiments show significant improvements in distinguishing adjacent teeth and tooth-gingiva boundaries, demonstrating that intraoral photographs serve as an efficient, semantically rich supplement to 3D scans for precise dental segmentation.
OMGTex: One-stage Multi-style Facial Texture Reconstruction without Geometry Guidance
Authors: Zitong Xiao; Yuda Qiu; Zisheng Ye; Xiaoguang Han
Venue: CVPR 2026 (main)

Abstract: We propose OMGTex, an end-to-end diffusion-based framework for reconstructing high-quality and editable facial UV textures from multi-style facial images. Existing texture reconstruction methods face two major limitations: (1) Fragility due to reliance on 3D geometry priors, which are difficult to estimate accurately, especially under facial occlusions or in stylized domains; and (2) A lack of semantic disentanglement, inhibiting region-specific texture editing and style transfer. Our work addresses both challenges simultaneously.Our core innovation is a geometry-free pipeline that directly maps a 2D face image to its corresponding editable UV texture. We introduce two key techniques: First, to address the challenge of UV misalignment common in diffusion generation, we introduce a gradient-guided refinement strategy at inference time, which explicitly corrects structural consistency. Second, we leverage the inherent semantic distribution capability of diffusion models and design a novel training paradigm to enhance this tendency, enabling semantic-aware editing of facial texture. Furthermore, to address the data scarcity in multi-style texture reconstruction, we construct CANVAS, the first comprehensive paired texture reconstruction dataset covering realistic and diverse stylized domains.To the best of our knowledge, OMGTex is the first geometry-free inference framework that achieves robust, style-consistent, and editable facial texture reconstruction across diverse domains. Our method achieves state-of-the-art performance on facial texture benchmarks. Codes and the pretrained model weights will be publicly released.
Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning
Authors: Sixian Zhang; Yiyao Wang; Xinhang Song; Keming Zhang; Zijian Xu; Shuqiang Jiang
Venue: CVPR 2026 (main)

Abstract: Understanding the geometric and semantic structure of environments is essential for embodied agents. Existing semantic mapping methods trade off between explicit geometry and multi-scale semantics,and lack a native interface for large models, thus requiring additional training of feature projection for semantic alignment. To this end, we propose the multi-scale Gaussian-Language Map (GLMap), which introduces three key designs: (1) explicit geometry, (2) multi-scale semantics covering both instance and region level concepts, and (3) a dual-modality interface where each semantic unit jointly stores a natural language description and a 3D Gaussian representation. The 3D Gaussians enable compact storage and fast rendering of task-relevant images via Gaussian splatting. To enable efficient incremental construction, we further propose a Gaussian Estimator that analytically derives Gaussian parameters from dense point clouds without gradient-based optimization. Experiments on ObjectNav, InstNav, and SQA tasks show that GLMap effectively enhances target localization and contextual reasoning, while remaining compatible with large-model-based methods in a zero-shot manner.
Can You Learn to See Without Images? Procedural Warm-Up for Vision Transformers
Authors: Zachary Shinnick; Liangze Jiang; Hemanth Saratchandran; Damien Teney; Anton van den Hengel
Venue: CVPR 2026 (main)

Abstract: Transformers are remarkably versatile, suggesting the existence of generic inductive biases beneficial across modalities. In this work, we explore a new way to instil such biases in vision transformers (ViTs) through pretraining on procedurally generated data devoid of visual or semantic content. We generate this data with simple algorithms such as formal grammars, so the results bear no relationship to either natural or synthetic images. We use this procedurally generated data to pretrain ViTs in a warm-up phase that bypasses their visual patch embedding mechanisms, thus encouraging the models to internalise abstract computational priors. When followed by standard image-based training, this warm-up significantly improves data efficiency, convergence speed, and downstream performance. On ImageNet-1K, for example, allocating just 1% of the training budget to procedural data improves final accuracy by over 1.7%. In terms of its effect on performance, 1% procedurally generated data is thus equivalent to 28% of the ImageNet-1K data. These findings suggest a promising path toward new data-efficient and domain-agnostic pretraining strategies.
LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis
Authors: Stanislaw Szymanowicz; Minghao Chen; Jianyuan Wang; Christian Rupprecht; Andrea Vedaldi
Venue: CVPR 2026 (main)

Abstract: Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive biases are still helpful in the design of such networks. We show this point by introducing LagerNVS, an encoder-decoder neural network for NVS that builds on `3D-aware' latent features. The encoder is initialized from a 3D reconstruction network pre-trained using explicit 3D supervision. This is paired with a lightweight decoder, and trained end-to-end with photometric losses. LagerNVS achieves state-of-the-art deterministic feed-forward Novel View Synthesis (including 31.4 PSNR on Re10k), with and without known cameras, renders in real time, generalizes to in-the-wild data, and can be paired with a diffusion decoder for generative extrapolation. See szymanowiczs.github.io/lagernvs for code, models, and examples.
Structure-Aware Representation Distillation for Tiny-Dense Object Segmentation
Authors: Xuesong Liu; Anke Xu; Wenbo Cao; Emmett Ientilucci
Venue: CVPR 2026 (main)

Abstract: Dense scenes containing numerous tiny objects pose a fundamental challenge for segmentation models, where small localization errors can significantly degrade downstream measurements. We present Structure-Aware Representation Distillation (SARD), a teacher-compatible framework that transfers structural knowledge from a large teacher to a compact student via feature-space alignment rather than mask imitation. SARD builds a structure-importance map by integrating boundary salience, geometric complexity, and local feature variation, and uses it to guide a unified representation loss that combines feature consistency with distribution alignment. This encourages the student to allocate capacity to geometrically informative regions while preserving global context. Experiments on Cityscapes, ADE20K, and a challenging rock fragmentation benchmark (RockFrag) show that SARD consistently improves both mIoU and boundary IoU over strong distillation baselines; on RockFrag, SARD improves a Swin-T student over CWD by +4.3 mIoU and +6.7 bIoU. A ResNet-50 student distilled from a Swin-L teacher achieves up to 7.7 times parameter reduction and 9 times higher throughput than the teacher, with no additional inference overhead beyond the student network, demonstrating that structure-aware representation distillation is effective and efficient for tiny-dense segmentation. Code is available at: https://github.com/liuuuuuuxuesong/SARD.
Linear Image Generation by Synthesizing Exposure Brackets
Authors: Yuekun Dai; Zhoutong Zhang; Shangchen Zhou; Nanxuan Zhao
Venue: CVPR 2026 (main)

Abstract: The life of a photo begins with photons striking the sensor, whose signals are passed through a sophisticated image signal processing (ISP) pipeline to produce a display-referred image. However, such images are no longer faithful to the incident light, being compressed in dynamic range and stylized by subjective preferences. In contrast, RAW images record direct sensor signals before non-linear tone mapping. After camera response curve correction and demosaicing, they can be converted into linear images, which are scene-referred representations that directly reflect true irradiance and are invariant to sensor-specific factors. Since image sensors have better dynamic range and bit depth, linear images contain richer information than display-referred ones, leaving users more room for editing during post-processing. Despite this advantage, current generative models mainly synthesize display-referred images, which inherently limits downstream editing. In this paper, we address the task of text-to-linear-image generation: synthesizing a high-quality, scene-referred linear image that preserves full dynamic range, conditioned on a text prompt, for professional post-processing. Generating linear images is challenging, as pre-trained VAEs in latent diffusion models struggle to simultaneously preserve extreme highlights and shadows due to the higher dynamic range and bit depth. To this end, we represent a linear image as a sequence of exposure brackets, each capturing a specific portion of the dynamic range, and propose a DiT-based flow-matching architecture for text-conditioned exposure bracket generation. We further demonstrate downstream applications including text-guided linear image editing and structure-conditioned generation via ControlNet.
Quota-Calibrated Fine-Grained Alignment with Context-Aware Marginals for Text-based Person Retrieval
Authors: Dongsheng Li; Xinyuan Guo; Huijie Zhang; Pingting Hao; Qiushi Xia
Venue: CVPR 2026 (main)

Abstract: The core challenge in Text-based Person Retrieval (TPR) lies in establishing fine-grained, many-to-many semantic alignment between textual words and visual regions. Existing methods predominantly rely on pointwise similarity or attention mechanisms, implicitly assuming matches are independent and balanced. Consequently, under conditions of attribute overlap and substantial background noise, these methods often misallocate matching weights to non-discriminative regions or words, resulting in ambiguous matching outcomes. To address this, we propose QC-Align, a quota-calibrated fine-grained alignment framework guided by context-aware marginals. Specifically, we propose a Context-Aware Marginal Estimator (CAME) that dynamically assigns "matching quotas" to each word and visual region, and subsequently employs a Quota-Calibrated Transport (QCT) objective to explicitly constrain the matching quality each word and region can carry, thereby jointly optimizing the many-to-many correspondence between text and vision under these constraints. Notably, QC-Align is a parameter-free, plug-and-play training regularizer that requires no fine-grained annotations and incurs no inference overhead. Experiments on multiple mainstream person retrieval benchmarks demonstrate that QC-Align consistently improves baseline model performance, with greater gains and better interpretability in few-shot and cross-domain scenarios.