CVPR 2026 论文列表

In this work, we propose the problem of localizing cameras and producing renders of a scene, given multiple images captured from ground/aerial/satellite viewpoints. We introduce a dataset called Sky2Ground, which contains synthetic/real images across all 3 viewpoints, along with camera parameters, and dense depth-maps/surface-normals. Recent works have shown that transformer-based nets like VGGT are capable of inferring scene-parameters in a single-forward pass. However, we formally reveal that simply fine-tuning such models reduces performance, and can't be solved simply by bruteforce-scaling. We find the culprit to be satellite images, which inject too much noise during the learning process. Therefore, we propose SkyNet to enable learning using satellite-images. SkyNet is a two-stream neural-net, with one stream explicitly processing satellite, and another processing all modalities together.We propose a restricted-attention mechanism, termed as `Masked-Satellite-Attention' which prevents ground/aerial images from interacting with satellite images. Further, our SkyNet is optimized with strategies inspired from curriculum-learning: sampling cameras which are far-away from each other during training. Extensive experiments on our Sky2Earth dataset reveal that SkyNet outperforms existing methods by 23% in terms of absolute performance. Our dataset, and code shall be made publicly available on huggingface.

Reliable 3D perception from multi-view roadside sensors hinges on the robust fusion of camera and LiDAR data, a task complicated by geometric misalignments and sensor calibration errors. This paper presents GSV2X, a fusion framework that tackles these challenges through two core contributions. First, to achieve robustness against spatial uncertainty, we lift 2D image features into a unified Bird's-Eye-View (BEV) space by representing them as 3D Gaussian distributions. By incorporating learnable perturbations guided by camera geometry, our model explicitly accounts for potential calibration inaccuracies. Second, to maximize the synergy between modalities, we propose a new orthogonal fusion module. This module employs constrained attention to enforce orthogonality between camera and LiDAR features, effectively disentangling redundant information and promoting the learning of complementary representations. Extensive experiments on the challenging RCooper dataset demonstrate that GSV2X sets a new state-of-the-art in multi-view roadside perception and exhibits remarkable robustness in complex, real-world scenarios.

This paper focuses on enhancing the grasping precision and generalization of manipulation policies learned via imitation learning. Diffusion-based policy learning methods have recently become the mainstream approach for robotic manipulation tasks. As grasping is a critical subtask in manipulation, the ability of imitation-learned policies to execute precise and generalizable grasps merits particular attention. Existing imitation learning techniques for grasping often suffer from imprecise grasp executions, limited spatial generalization, and poor object generalization. To address these challenges, we incorporate grasp prior knowledge into the diffusion policy framework. In particular, we employ a latent diffusion policy to guide action chunk decoding with grasp pose prior, ensuring that generated motion trajectories adhere closely to feasible grasp configurations. Furthermore, we introduce a self-supervised reconstruction objective during diffusion to embed the graspness prior: at each reverse diffusion step, we reconstruct wrist-camera images back-projected the graspness from the intermediate representations. Both simulation and real robot experiments demonstrate that our approach significantly outperforms baseline methods and exhibits strong dynamic grasping capabilities.

Self-supervised contrastive learning has emerged as a powerful paradigm for skeleton-based action recognition by enforcing consistency in the embedding space. However, existing methods rely on binary contrastive objectives that overlook the intrinsic continuity of human motion, resulting in fragmented feature clusters and rigid class boundaries. To address these limitations, we propose TranCLR, a Transitional anchor-based Contrastive Learning framework that captures the continuous geometry of the action space. Specifically, the proposed Action Transitional Anchor Construction (ATAC) explicitly models the geometric structure of transitional states to enhance the model's perception of motion continuity. Building upon these anchors, a Multi-Level Geometric Manifold Calibration (MGMC) mechanism is introduced to adaptively calibrate the action manifold across multiple levels of continuity, yielding a smoother and more discriminative representation space. Extensive experiments on the NTU RGB+D, NTU RGB+D 120 and PKU-MMD datasets demonstrate that TranCLR achieves superior accuracy and calibration performance, effectively learning continuous and uncertainty-aware skeleton representations. Code will be made publicly available.

Point cloud completion is an important yet challenging problem in 3D computer vision, which aims to reconstruct complete and dense 3D shapes from partial point clouds. Although transformer-based and geometry-based approaches have made significant progress, they often struggle to capture the complex, high-order correlations inherent in point clouds. To address this limitation, we propose Hyper-PCN, a point cloud completion framework that leverages hypergraphs to explicitly model complex, higher-order correlations within incomplete inputs for more accurate completion. It comprises two key modules: Hyper Refinement Stack, designed to progressively capture coarse-to-fine high-order correlations through a series of hypergraph learning stages, and Anchor-based Hypergraph Neural Network, which employs a two-stage sampling strategy to construct collaborative hypergraphs, ensuring robust modeling of global structures. Extensive experiments on multiple datasets demonstrate that our approach consistently outperforms state-of-the-art methods.

Compound activity modeling is critical for drug discovery, where accurate *in silico* predictions can significantly reduce reliance on expensive, time-consuming target-specific experimental assays. Traditional machine learning approaches for compound activity modeling typically rely on either chemoproteomics-centric molecular data or phenotype-centric imaging screens, limiting their ability to capture complementary biological signals. While multimodal approaches show promise, they often fail to capture the interplay between molecular mechanisms and cellular responses. In this paper, we present **BiGMINT**, a **Bi**ologically **G**uided **M**ultimodal framework that hierarchically **INT**egrates chemoproteomic and high-content imaging (HCI) data, introducing chemoproteomics-guided phenotypic aggregation, task-aware cross-modal fusion, and protein-protein interaction priors for modeling activities. On two large-scale in-house datasets, with 99K and 40K compound-HCI pairs from U2OS and iNeuron, **BiGMINT** improves mean AUCROC by up to 10.0% and 4.2%, and high-performing task coverage by up to 103% and 56% over best unimodal and multimodal methods. Thorough analysis revealed mechanistic insights, showing these gains stem from modality complementarity, and protein-protein priors enhance modeling of challenging activities.

Atmospheric turbulence significantly degrades long-range imaging by introducing geometric warping and exposure-time-dependent blur, which adversely affects both visual quality and the performance of high-level vision tasks. Existing methods for synthesizing turbulence effects often oversimplify the relationship between blur and exposure-time, typically assuming fixed or binary exposure settings. This leads to unrealistic synthetic data and limited generalization capability of trained models. To address this gap, we revisit the modulation transfer function (MTF) formulation and propose a novel Exposure-Time-dependent MTF (ET-MTF) that models blur as a continuous function of exposure-time. For blur synthesis, we derive a tilt-invariant point spread function (PSF) from the ET-MTF, which, when integrated with a spatially varying blur-width field, provides a comprehensive and physically accurate characterization of turbulence-induced blur. Building on this synthesis pipeline, we construct ET-Turb, a large-scale synthetic turbulence dataset that explicitly incorporates continuous exposure-time modeling across diverse optical and atmospheric conditions. The dataset comprises 5,083 videos (2,005,835 frames), partitioned into 3,988 training and 1,095 test videos. Extensive experiments demonstrate that models trained on ET-Turb produce more realistic restorations and achieve superior generalization on real-world turbulence data compared to those trained on other datasets. The dataset is publicly available at: github.com/Jun-Wei-Zeng/ET-Turb.

Zero-shot unsupervised reinforcement learning (URL) offers a promising direction for building generalist agents capable of generalizing to unseen tasks without additional supervision. Among existing approaches, successor representations (SR) have emerged as a prominent paradigm due to their effectiveness in structured, low-dimensional settings. However, SR methods struggle to scale to high-dimensional visual environments. Through empirical analysis, we identify two key limitations of SR in visual URL: (1) SR objectives often lead to suboptimal representations that attend to dynamics-irrelevant regions, resulting in inaccurate successor measures and degraded task generalization; and (2) these flawed representations hinder SR policies from modeling multi-modal skill-conditioned action distributions and ensuring skill controllability. To address these limitations, we propose Saliency-Guided Representation with Consistency Policy Learning (SRCP), a novel framework that improves zero-shot generalization of SR methods in visual URL. SRCP decouples representation learning from successor training by introducing a saliency-guided dynamics task to capture dynamics-relevant representations, thereby improving successor measure and task generalization. Moreover, it integrates a fast-sampling consistency policy with URL-specific classifier-free guidance and tailored training objectives to improve skill-conditioned policy modeling and controllability. Extensive experiments on 16 tasks across 4 datasets from the ExORL benchmark demonstrate that SRCP achieves state-of-the-art zero-shot generalization in visual URL and is compatible with various SR methods.

Recently, the community has witnessed significant progress in human modeling from single or multi-view inputs. However, these approaches often rely on guessing the occluded regions through either generative models or template fitting. In this work, we address these challenges by exploring optimal fusion strategies using only sparse multi-view inputs. We propose SMVRT, an end-to-end implicit 3D reconstruction framework for sparse multi-view human modeling. Our key contribution lies in the fusion blocks at three stages of the network. First, local and global features alternative fusion modules are designed to enhance 2D features. Second, attentional fusion is performed on warped multi-view and multi-level 2D features to form 3D feature grid. The feature grid aggregates spatially coherent multi-view features by 3D regularization. Third, attentional 2D-3D feature aggregation generates the enhanced latent embeddings for query points to decode occupancies. Experiments on the THUman 2.0/2.1, MultiGarment and MultiHuman datasets demonstrate that our system significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

Learning new robot tasks on new platforms and in new scenes from only a handful of demonstrations remains challenging. While videos of other embodiments---humans and different robots---are abundant, differences in embodiment, camera, and environment hinder their direct use. We address the small-data problem by introducing a unifying, symbolic representation---a compact 3D "trace-space" of scene-level trajectories---that enables learning from cross-embodiment, cross-environment, and cross-task videos. We present TraceGen, a world model that predicts future motion in trace-space rather than pixel space, abstracting away appearance while retaining the geometric structure needed for manipulation. To train TraceGen at scale, we develop TraceForge, a data pipeline that transforms heterogeneous human and robot videos into consistent 3D traces, yielding a corpus of 123K videos and 1.8M observation--trace--language triplets. Pretraining on this corpus produces a transferable 3D motion prior that adapts efficiently: with just five target robot videos, TraceGen attains 80% success across four tasks while offering 50-600x faster inference than state-of-the-art video-based world models. In the more challenging case where only five uncalibrated human demonstration videos captured on a handheld phone are available, it still reaches 67.5% success on a real robot, highlighting TraceGen's ability to adapt across embodiments without heavy pixel-space generation.

Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. This indicates the preceding semantics potentially benefit the texture generation by providing a semantic anchor. Recent advances have integrated semantic priors from pretrained visual encoders to further enhance LDMs, yet they still denoise semantic and VAE-encoded texture synchronously, neglecting such ordering. Observing these, we propose Semantic-First Diffusion (SFD), a latent diffusion paradigm that explicitly prioritizes semantic formation. SFD first constructs composite latents by combining the compact semantic latent, which is extracted from pretrained visual encoder via a dedicated Semantic VAE, with the texture latent. The core of SFD is to denoise the semantic and texture latents asynchronously using separate noise schedules: semantics precede textures by a temporal offset, providing clearer high-level guidance for texture refinement and enabling natural coarse-to-fine generation. On ImageNet 256x256 with guidance, SFD achieves FID 1.06 (LightningDiT-XL) and FID 1.04 (1.0B LightningDiT-XXL), while achieving up to 100xfaster convergence than original DiT without guidance. SFD also improves existing methods like ReDi and VA-VAE, demonstrating the effectiveness of asynchronous, semantics-led modeling.

Recent advances in 3D Gaussian Splatting (3DGS) enable photorealistic real-time rendering but also increase the risks of unauthorized copying and redistribution. Existing 3DGS watermarking methods typically rely on handcrafted thresholds or globally fixed hyperparameters to balance invisibility and robustness, making their embedding behavior static and scene-agnostic. We instead formulate 3DGS watermarking as a goal-directed decision process and introduce Write Where It Matters (W2M), the first reinforcement learning-based framework that adaptively learns where and how much to embed. By modeling the embedding process as a Markov Decision Process, W2M uses a lightweight policy network to allocate precise Gaussian updates directly from immediate reward feedback, iteratively. The reward incentivizes both rendering-space invisibility and decoding robustness under various image- and model-level distortions. To achieve efficiency, W2M operates on a structured 3DGS backbone organized around learnable anchors and applies policy-guided per-anchor gradient scaling. Extensive experiments across the Blender, LLFF, and Mip-NeRF 360 datasets demonstrate that W2M achieves state-of-the-art bit accuracy, strong perceptual fidelity, and structural consistency under both standard and adversarial conditions.

Generating high-fidelity 3D contents remains a fundamental challenge due to the complexity of representing arbitrary topologies--such as open surfaces and intricate internal structures--while preserving geometric details. Prevailing methods based on signed distance fields (SDFs) are hampered by costly watertight preprocessing and struggle with non-manifold geometries, while point-cloud representations often suffer from sampling artifacts and surface discontinuities. To overcome these limitations, we propose a novel 3D variational autoencoder (VAE) framework built upon unsigned distance fields (UDFs)--a more robust and computationally efficient representation that naturally handles complex and incomplete shapes. Our core innovation is a local-to-global (LoG) architecture that processes the UDF by partitioning it into uniform subvolumes, termed UBlocks. This architecture couples 3D convolutions for capturing local detail with sparse transformers for enforcing global coherence. A Pad-Average strategy further ensures smooth transitions at subvolume boundaries during reconstruction. This modular design enables seamless scaling to ultra-high resolutions up to 2048^3-a regime previously unattainable for 3D VAEs. Experiments demonstrate state-of-the-art performance in both reconstruction accuracy and generative quality, yielding superior surface smoothness and geometric flexibility.

Humans perceive the 3D world from limited 2D observations. While recent feed-forward generalizable 3D reconstruction models can recover structures from sparse images, they typically represent only observed regions, leaving unseen geometry unmodeled. This raises a fundamental question: Can we infer complete 3D structure from partial observations? We present RnG (Reconstruction and Generation), a feed-forward Transformer that unifies reconstruction and generation by predicting an implicit, complete 3D representation. RnG introduces a reconstruction-guided causal attention mechanism that separates reconstruction and generation at the attention level while treating the KV-cache as an implicit 3D representation. Arbitrary poses can efficiently query this cache to render high-fidelity RGBD outputs. RnG accurately reconstructs visible geometry while generating plausible unseen structures and appearance. It achieves state-of-the-art performance in generalizable 3D reconstruction and novel view generation, while being efficient enough for real-time interactive applications.

Mix-supervised image segmentation aims to effectively leverage heterogeneous annotations. Recent prompt-based advances utilize foundation models such as Segment Anything Model (SAM) to generate pseudo-masks by treating weak labels as spatial prompts. However, these methods rely heavily on sparse spatial priors, leading to suboptimal performance in ambiguous regions and overlooking the potential of unlabeled data due to the absence of promptable cues. In this paper, we propose SAMIX, a novel framework that adapts SAM2 into a semantic-aware pseudo-label generator SA-SAM2 by incorporating a lightweight semantic adapter. Beyond being guided by sparse spatial prompts, SA-SAM2 facilitates dense contextual prompts provided by valuable image-mask reference pairs with shared semantics. Another core component of SAMIX is the Selecting Policy Network (SPNet), which auto-regressively retrieves relevant and complementary reference samples for each query image. Unlike rule-based selections, SPNet is trained via reinforcement learning to actively explore reference combinations that maximize pseudo-label quality. Guided by customized and verifiable rewards associated with mask quality, the selection is directed toward semantically informative and diverse contexts. We conduct extensive experiments on two general datasets (PASCAL VOC 2012 and Cityscapes) and two specific datasets with ambiguous boundaries (camouflaged object detection and image polyp segmentation). Across diverse mix-supervision settings, SAMIX consistently achieves state-of-the-art performance. Codes are available at https://github.com/Huster-Hq/SAMIX.

Occlusion remains one of the major challenges in UAV tracking, where dynamic viewpoints and complex environments often cause partial or complete visibility loss.Existing transformer-based trackers typically regard occlusion as random information dropout, overlooking its structured and spatially correlated nature in real-world scenes.We rethink occlusion modeling in UAV tracking as a structured process governed by spatial dependencies.Based on this insight, we introduce Clustered Occlusion Modeling (COM) to generate realistic, density-adaptive occlusion patterns that enhance feature robustness under partial visibility.Furthermore, we design Cost-Aware Depth Bias (CADB), which employs a depth-dependent prior to adjust inference depth, yielding better efficiency while maintaining competitive accuracy.Integrating COM and CADB into a unified single-stream transformer framework, termed OCTrack, our tracker achieves robust and efficient UAV tracking in occlusion-prone environments.Extensive experiments on multiple UAV benchmarks validate its effectiveness and demonstrate state-of-the-art performance.