GenAI4ELab/papercli-papers huggingface dataset:
FMPose3D: monocular 3D pose estimation via flow matching
Authors: Ti Wang; Xiaohang Yu; Mackenzie Weygandt Mathis
Venue: CVPR 2026 (main)

Abstract: Monocular 3D pose estimation is fundamentally ill-posed due to depth ambiguity and occlusions, thereby motivating probabilistic methods that generate multiple plausible 3D pose hypotheses. In particular, diffusion-based models have recently demonstrated strong performance, but their iterative denoising process typically requires many timesteps for each prediction, making inference computationally expensive. In contrast, we leverage Flow Matching (FM) to learn a velocity field defined by an Ordinary Differential Equation (ODE), enabling efficient generation of 3D pose samples with only a few integration steps. We propose a novel generative pose estimation framework, FMPose3D, that formulates 3D pose estimation as a conditional distribution transport problem. It continuously transports samples from a standard Gaussian prior to the distribution of plausible 3D poses conditioned only on 2D inputs. Although ODE trajectories are deterministic, FMPose3D naturally generates various pose hypotheses by sampling different noise seeds. To obtain a single accurate prediction from those hypotheses, we further introduce a Reprojection-based Posterior Expectation Aggregation (RPEA) module, which approximates the Bayesian posterior expectation over 3D hypotheses. FMPose3D surpasses existing methods on the widely used human pose estimation benchmarks Human3.6M and MPI-INF-3DHP, and further achieves state-of-the-art performance on the 3D animal pose datasets Animal3D and CtrlAni3D, demonstrating strong performance across both 3D pose domains. The code is available at https://github.com/AdaptiveMotorControlLab/FMPose3D.
VABench: A Comprehensive Benchmark for Audio-Video Generation
Authors: Daili Hua; Xizhi Wang; Bohan Zeng; Xinyi Huang; Hao Liang; Junbo Niu; Xinlong Chen; Quanqing Xu; Wentao Zhang
Venue: CVPR 2026 (main)

Abstract: Recent advances in video generation have been remarkable, enabling models to produce visually compelling videos with synchronized audio. While existing video generation benchmarks provide comprehensive metrics for visual quality, they lack convincing evaluations for audio-video generation, especially for models aiming to generate synchronized audio-video outputs. To address this gap, we introduce VABench, a comprehensive and multi-dimensional benchmark framework designed to systematically evaluate the capabilities of synchronous audio-video generation. VABench encompasses three primary task types: text-to-audio-video (T2AV), image-to-audio-video (I2AV), and stereo audio-video generation. It further establishes two major evaluation modules covering 15 dimensions. These dimensions specifically assess pairwise similarities (text-video, text-audio, video-audio), audio-video synchronization, lip-speech consistency, and carefully curated audio and video question-answering (QA) pairs, among others. Furthermore, VABench covers seven major content categories: animals, human sounds, music, environmental sounds, synchronous physical sounds, complex scenes, and virtual worlds. We provide a systematic analysis and visualization of the evaluation results, aiming to establish a new standard for assessing video generation models with synchronous audio capabilities and to promote the comprehensive advancement of the field.
Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models
Authors: Qifan Li; Xingyu Zhou; Jinhua Zhang; Weiyi You; Shuhang Gu
Venue: CVPR 2026 (main)

Abstract: Latent diffusion models have emerged as the dominant framework for high-fidelity and efficient image generation, owing to their ability to learn diffusion processes in compact latent spaces. However, while previous research has focused primarily on reconstruction accuracy and semantic alignment of the latent space, we observe that another critical factor, robustness to sampling perturbations, also plays a crucial role in determining generation quality. Through empirical and theoretical analyses, we show that the commonly used b-VAE-based tokenizers in latent diffusion models, tend to produce overly compact latent manifolds that are highly sensitive to stochastic perturbations during diffusion sampling, leading to visual degradation. To address this issue, we propose a simple yet effective solution that constructs a latent space robust to sampling perturbations while maintaining strong reconstruction fidelity. This is achieved by introducing a Variance Expansion loss that counteracts variance collapse and leverages the adversarial interplay between reconstruction and variance expansion to achieve an adaptive balance that preserves reconstruction accuracy while improving robustness to stochastic sampling. Extensive experiments demonstrate that our approach consistently enhances generation quality across different latent diffusion architectures, confirming that robustness in latent space is a key missing ingredient for stable and faithful diffusion sampling. Our project page:  https://github.com/CVL-UESTC/VE-Loss .
Seeing Beyond: Extrapolative Domain Adaptive Panoramic Segmentation
Authors: Yuanfan Zheng; Kunyu Peng; Xu Zheng; Kailun Yang
Venue: CVPR 2026 (main)

Abstract: Cross-domain panoramic semantic segmentation has attracted growing interest as it enables comprehensive 360^ \circ  scene understanding for real-world applications. However, it remains particularly challenging due to severe geometric  Field of View (FoV) distortions and inconsistent open-set semantics across domains. In this work, we formulate an open-set domain adaptation setting,  and propose Extrapolative Domain Adaptive Panoramic Segmentation (EDA-PSeg) framework that trains on local perspective views and tests on full 360^ \circ  panoramic images, explicitly tackling both geometric FoV shifts across domains and semantic uncertainty arising from previously unseen classes. To this end, we propose the Euler-Margin Attention (EMA), which introduces an angular margin to enhance viewpoint-invariant semantic representation, while performing amplitude and phase modulation to improve generalization toward unseen classes. Additionally, we design the Graph Matching Adapter (GMA), which builds high-order graph relations to align shared semantics across FoV shifts while effectively separating novel categories through structural adaptation. Extensive experiments on four benchmark datasets under camera-shift, weather-condition, and open-set scenarios demonstrate that EDA-PSeg achieves state-of-the-art performance, robust generalization to diverse viewing geometries, and resilience under varying environmental conditions. The code is available at https://github.com/zyfone/EDA-PSeg.
PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splatting
Authors: Yixiao Song; Qingyong Li; Wen Wang; Zhicheng Yan
Venue: CVPR 2026 (main)

Abstract: Unsupervised point cloud segmentation is critical for embodied artificial intelligence and autonomous driving, as it mitigates the prohibitive cost of dense point-level annotations required by fully supervised methods. While integrating 2D pre-trained models such as the Segment Anything Model (SAM) to supplement semantic information is a natural choice, yet this approach faces a fundamental mismatch between discrete 3D points and continuous 2D images. This mismatch leads to inevitable projection overlap and complex modality alignment, resulting in compromised semantic consistency across 2D-3D transfer. To address these limitations, this paper proposes PointGS, a simple yet effective pipeline for unsupervised 3D point cloud segmentation. PointGS leverages 3D Gaussian Splatting as a unified intermediate representation to bridge the discrete-continuous domain gap. Input sparse point clouds are first reconstructed into dense 3D Gaussian spaces via multi-view observations, filling spatial gaps and encoding occlusion relationships to eliminate projection-induced semantic conflation. Multi-view dense images are rendered from the Gaussian space, with 2D semantic masks extracted via SAM, and semantics are distilled to 3D Gaussian primitives through contrastive learning to ensure consistent semantic assignments across different views. The Gaussian space is aligned with the original point cloud via two-step registration, and point semantics are assigned through nearest-neighbor search on labeled Gaussians. Experiments demonstrate that PointGS outperforms state-of-the-art unsupervised methods, achieving +0.9% mIoU on ScanNet-V2 and +2.8% mIoU on S3DIS.
A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World
Authors: Jikang Cheng; Renye Yan; Zhiyuan Yan; Yaozhong Gan; Xueyi Zhang; Zhongyuan Wang; Wei Peng; Ling Liang
Venue: CVPR 2026 (main)

Abstract: Existing methods for deepfake detection aim to develop generalizable detectors. Although "generalizable" could be the ultimate target once and for all, with limited training forgeries and domains, it appears idealistic to expect generalization that covers entirely unseen variations, especially given the diversity, advancement, and vast volume of real-world deepfakes. Therefore, introducing large-scale multi-domain data for training can be feasible and important for real-world applications.However, within such a multi-domain scenario, the differences between multiple domains, rather than the subtle real/fake distinctions, dominate the feature space. As a result, despite detectors being able to relatively separate real and fake within each domain (i.e., high AUC), they struggle with single-image real/fake judgments in domain-unspecified conditions (i.e., low ACC).In this paper, we first define a new research paradigm named Multi-In-Domain Face Forgery Detection (MID-FFD), which includes sufficient volumes of real-fake domains for training. Then, the detector should provide definitive real-fake judgments to the domain-unspecified inputs, which simulate the frame-by-frame independent detection scenario in the real world. Meanwhile, to address the domain-dominant issue, we propose a two-stage, model-agnostic framework termed DevDet (\underline Dev eloper for \underline Det ector) to amplify real/fake differences and make them dominant in the feature space. DevDet consists of a Face Forgery Developer (FFDev) and a Dose-Adaptive detector Fine-Tuning strategy (DAFT). Experiments demonstrate our superiority in effectively predicting real-fake under the MID-FFD scenario while maintaining original generalization ability to unseen data.
DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images
Authors: Xiaoxue Chen; Ziyi Xiong; Yuantao Chen; Gen Li; Nan Wang; Hongcheng Luo; Long Chen; Haiyang Sun; Bing Wang; Guang Chen; Hongyang Li; Ya-Qin Zhang; Hangjun Ye; Hao Zhao
Venue: CVPR 2026 (main)

Abstract: Autonomous driving needs fast, scalable 4D reconstruction and re-simulation for training and evaluation, yet most methods for dynamic driving scenes still rely on per-scene optimization, known camera calibration, or short frame windows, making them slow and impractical. We revisit this problem from a feedforward perspective and introduce Driving Gaussian Grounded Transformer (DGGT), a unified framework for pose-free dynamic scene reconstruction. We note that the existing formulations, treating camera pose as a required input, limit flexibility and scalability. Instead, we reformulate pose as an output of the model, enabling reconstruction directly from sparse, unposed images and supporting an arbitrary number of views for long sequences. Our approach jointly predicts per-frame 3D Gaussian maps and camera parameters, disentangles dynamics with a lightweight dynamic head, and preserves temporal consistency with a lifespan head that modulates visibility over time. A diffusion-based rendering refinement further reduces motion/interpolation artifacts and improves novel-view quality under sparse inputs. The result is a single-pass, pose-free algorithm that achieves state-of-the-art performance and speed. Trained and evaluated on large-scale driving benchmarks (Waymo, nuScenes, Argoverse2), our method outperforms prior work both when trained on each dataset and in zero-shot transfer across datasets, and it scales well as the number of input frames increases.
ProPhy: Progressive Physical Alignment for Dynamic World Simulation
Authors: Zijun Wang; Panwen Hu; Jing Wang; Terry Jingchen Zhang; Yuhao Cheng; Long Chen; Yiqiang Yan; Zutao Jiang; Hanhui Li; Xiaodan Liang
Venue: CVPR 2026 (main)

Abstract: Recent advances in video generation have shown remarkable potential for constructing world simulators. However, current models still struggle to produce physically consistent results, particularly when handling large-scale or complex dynamics. This limitation arises primarily because existing approaches respond isotropically to physical prompts and neglect the fine-grained alignment between generated content and localized physical cues. To address these challenges, we propose ProPhy, a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation. ProPhy employs a two-stage Mixture-of-Physics-Experts (MoPE) mechanism for discriminative physical prior extraction, where Semantic Experts infer semantic-level physical principles from textual descriptions, and Refinement Experts capture token-level physical dynamics. This mechanism allows the model to learn fine-grained, physics-aware video representations that better reflect underlying physical laws. Furthermore, we introduce a physical alignment strategy that transfers the physical reasoning capabilities of vision-language models (VLMs) into the Refinement Experts, facilitating a more accurate representation of dynamic physical phenomena. Extensive experiments on physics-aware video generation benchmarks demonstrate that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.
FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision
Authors: Zekai Wu; Shuqi Fan; Mengyin Liu; Yuhua Luo; Xincheng Lin; Ming Yan; Junhao Wu; Xiuhong Lin; Yuexin Ma; Chenglu Wen; Lan Xu; Siqi Shen; Cheng Wang
Venue: CVPR 2026 (main)

Abstract: Precise motion timing (PMT) is crucial for swift motion analysis. A millisecond difference may determine victory or defeat in sports competitions. Despite substantial progress in human pose estimation (HPE), PMT remains largely overlooked by the HPE community due to the limited availability of high-temporal-resolution labeled datasets. Today, PMT is achieved using high-speed RGB cameras in specialized scenarios such as the Olympic Games; however, their high costs, light sensitivity, bandwidth, and computational complexity limit their feasibility for daily use. We developed FlashCap, the first flashing LED-based MoCap system for PMT. With FlashCap, we collect a millisecond-resolution human motion dataset, FlashMotion, comprising the event, RGB, LiDAR, and IMU modalities, and demonstrate its high quality through rigorous validation. To evaluate the merits of FlashMotion, we perform two tasks: precise motion timing and high-temporal-resolution HPE. For these tasks, we propose ResPose, a simple yet effective baseline that learns residual poses based on events and RGBs. Experimental results show that ResPose reduces pose estimation errors by  40% and achieves millisecond-level timing accuracy, enabling new research opportunities. The dataset and code will be shared with the community.
Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping
Authors: Tianxiang Du; Hulingxiao He; Yuxin Peng
Venue: CVPR 2026 (main)

Abstract: The widespread use of smartphones has made photography ubiquitous, yet a clear gap remains between ordinary users and professional photographers, who can identify aesthetic issues and provide actionable shooting guidance during capture. We define this capability as aesthetic guidance (AG) --- an essential but largely underexplored domain in computational aesthetics. Existing multimodal large language models (MLLMs) primarily offer overly positive feedback, failing to identify issues or provide actionable guidance. Without AG capability, they cannot effectively identify distracting regions or optimize compositional balance, thus also struggling in aesthetic cropping, which aims to refine photo composition through reframing after capture. To address this, we introduce AesGuide, the first large-scale AG dataset and benchmark with 10,748 photos annotated with aesthetic scores, analyses, and guidance. Building upon it, we propose Venus, a two-stage framework that first empowers MLLMs with AG capability through progressively complex aesthetic questions and then activates their aesthetic cropping power via CoT-based rationales. Extensive experiments show that Venus substantially improves AG capability and achieves state-of-the-art (SOTA) performance in aesthetic cropping, enabling interpretable and interactive aesthetic refinement across both stages of photo creation. Code is available at https://github.com/PKU-ICST-MIPL/Venus_CVPR2026.
IEBGL:An Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and Literature-Augmented Semantics
Authors: Yihang Duan; Shuo Huang; Li Zhang; Meiling Wang; Li Zhang
Venue: CVPR 2026 (main)

Abstract: Resting-state functional MRI (rs-fMRI) provides rich information for modeling brain connectivity in disease diagnosis. However, most existing brain graph learning methods rely solely on imaging data, leading to limited biological interpretability and poor integration of external medical knowledge. To address these challenges, we propose an Interpretability-Enhanced Brain Graph Learning (IEBGL) framework that anchors brain network modeling in large-scale medical knowledge. Our framework introduces two complementary modules: LLM-Instructed Topological Reconstruction (LITR) and Literature-Augmented Semantic Aggregation (LASA). LITR employs large language model (LLM) reasoning to refine brain connectivity and construct topological structure. LASA augments node representations by aggregating semantic information from biomedical literature, ensuring the model's interpretability and relevance to clinical disease knowledge. Finally, the framework is trained with the Graph Bi-directional Mamba Network (GBMN) for disease diagnosis. Extensive experiments on the REST-meta-MDD and ABIDE datasets, together with 35,133 depression-related and 32,617 autism-related publications, demonstrate that IEBGL outperforms state-of-the-art methods in classification performance. Further analyses show that the LITR module reveals biologically meaningful alterations in brain connectivity, while the LASA module establishes interpretable associations between these regions and disease-related biomedical literature. Together, these mechanisms help IEBGL explain abnormal brain connections and their links to disease-related knowledge.
Semantic Scale Space: A Framework for Controllable Image Abstraction
Authors: Kazu Mishiba
Venue: CVPR 2026 (main)

Abstract: Image abstraction, a fundamental component of non-photorealistic rendering (NPR), aims to simplify photographs into stylized depictions while preserving perceptually important structures. A central difficulty is selectivity: removing fine textures while preserving semantically meaningful boundaries. Existing approaches often expose only a few entangled controls, so the amount of smoothing and the set of preserved structures cannot be adjusted in a clear and intuitive manner. We propose the Semantic Scale Space (SSS), a two-dimensional abstraction framework parameterized by smoothing strength and semantic granularity. SSS externalizes the stopping set by using a controllable semantic boundary detector to specify which structures act as barriers to smoothing, independently of how far simplification proceeds within homogeneous regions. We further introduce Adaptive Granularity Scheduling Smoothing (AGSS), a concrete traversal policy in this space that combines a donor-gated diffusion operator with a fine-to-coarse granularity schedule. To evaluate selectivity fairly, we also introduce an effect-matched protocol based on a Region Homogeneity Index, which compares methods under matched smoothing levels. On the SBD benchmark, AGSS achieves higher boundary preservation and lower geometric drift than strong baselines at the same degree of smoothing. On DIV2K, a user study further shows that downstream NPR results built on AGSS-based abstractions are consistently preferred. These results demonstrate that SSS and AGSS provide a practical framework and method for controllable image abstraction in modern creative applications.
E-RayZer: Self-supervised 3D Reconstruction as Spatial Visual Pre-training
Authors: Qitao Zhao; Hao Tan; Qianqian Wang; Sai Bi; Kai Zhang; Kalyan Sunkavalli; Shubham Tulsiani; Hanwen Jiang
Venue: CVPR 2026 (main)

Abstract: Self-supervised pre-training has driven rapid progress in foundation models for language, 2D images, and video, yet remains largely unexplored for learning 3D-aware representations from multi-view images. In this paper, we present E-RayZer, a self-supervised 3D vision model that learns geometrically grounded representations directly from unlabeled images. Unlike prior self-supervised methods such as  RayZer, which infer 3D indirectly through latent-space view synthesis, E-RayZer operates directly in 3D space, performing self-supervised 3D reconstruction with Explicit geometry. This formulation eliminates shortcut solutions and yields representations that are 3D-aware. To ensure convergence and scalability, we introduce a fine-grained learning curriculum that organizes training from easy to hard samples and harmonizes heterogeneous data sources without any supervision. Experiments show that E-RayZer significantly outperforms RayZer on pose estimation and matches or sometimes surpasses fully supervised reconstruction models such as VGGT. Furthermore, its learned representations outperform leading visual pre-training models (e.g., DINOv3, CroCo v2, VideoMAE V2, and RayZer) on 3D downstream tasks, establishing E-RayZer as a promising paradigm for spatial visual pre-training.
x^2-Fusion: Cross-Modality and Cross-Dimension Flow Estimation in Event Edge Space
Authors: Ruishan Guo; Ciyu Ruan; Haoyang Wang; Zihang Gong; Jingao Xu; Xinlei Chen
Venue: CVPR 2026 (main)

Abstract: Estimating dense 2D optical flow and 3D scene flow is essential for dynamic scene understanding. Recent work combines images, LiDAR, and event data to jointly predict 2D and 3D motion, yet most approaches operate in separate heterogeneous feature spaces. Without a shared latent space that all modalities can align to, these systems rely on multiple modality-specific blocks, leaving cross-sensor mismatches unresolved and making fusion unnecessarily complex. Event cameras naturally provide a spatiotemporal edge signal, which we can treat as an intrinsic edge field to anchor a unified latent representation, termed the Event Edge Space. Building on this idea, we introduce x^2-Fusion, which reframes multimodal fusion as representation unification: event-derived spatiotemporal edges define an edge-centric homogeneous space, and image and LiDAR features are explicitly aligned in this shared representation. Within this space, we perform reliability-aware adaptive fusion to estimate modality reliability and emphasize stable cues under degradation. We further employ cross-dimension contrast learning to tightly couple 2D optical flow with 3D scene flow. Extensive experiments on both synthetic and real benchmarks show that x^2-Fusion achieves state-of-the-art accuracy under standard conditions and delivers substantial improvements in challenging scenarios.