CVPR 2026 论文列表

Online 3D reconstruction from streaming inputs requires both long-term temporal consistency and efficient memory usage. Although causal variants of VGGT address this challenge through a key-value (KV) cache mechanism, the cache grows linearly with the stream length, creating a major memory bottleneck. Under limited memory budgets, early cache eviction significantly degrades reconstruction quality and temporal consistency. In this work, we observe that attention in causal transformers for 3D reconstruction exhibits intrinsic spatio-temporal sparsity. Based on this insight, we propose STAC, a Spatio-Temporally Aware Cache Compression framework for streaming 3D reconstruction with large causal transformers. STAC consists of three key components: (1) a Working Temporal Token Caching mechanism that preserves long-term informative tokens using decayed cumulative attention scores; (2) a Long-term Spatial Token Caching scheme that compresses spatially redundant tokens into voxel-aligned representations for memory-efficient storage; and (3) a Chunk-based Multi-frame Optimization strategy that jointly processes consecutive frames to improve temporal coherence and GPU efficiency. Extensive experiments show that STAC achieves state-of-the-art reconstruction quality while reducing memory consumption by nearly 10x and accelerating inference by 4x, substantially improving the scalability of real-time 3D reconstruction in streaming settings.

Aerial-Ground Person Re-Identification (AGPReID) remains highly challenging due to drastic viewpoint variations between drones and fixed cameras. Existing methods typically follow a view-invariant paradigm, aligning shared features across views to achieve robustness. However, view-invariant inherently enforces part-level alignment, which ignores view-specific cues and discriminative identity information. To this end, this work proposes ViSA (View-aware Semantic Alignment), a view-aware framework that achieves cross-view semantic consistency containing an Expert-driven Token Generation Module (ETGM) and a Dual-branch Local Fusion Module (DLFM). Technically, the former constructs a set of view-aware experts to generate adaptive semantic queries that perceive viewpoint-specific patterns, while the latter leverages graph reasoning to extract and align local regions responsive to different experts under varying viewpoints. Extensive experiments on three large-scale AGPReID benchmarks including AG-ReID.v2, CARGO and LAGPeR demonstrate that ViSA consistently achieves superior performance, with a notable 10.06% mAP improvement on the challenging CARGO cross-view protocol. The code will be available.

Users frequently edit camera images post-capture to achieve their preferred photofinishing style. While editing in the RAW domain provides greater accuracy and flexibility, most edits are performed on the camera's display-referred output (e.g., 8-bit sRGB JPEG) since RAW images are rarely stored. Existing RAW reconstruction methods can recover RAW data from sRGB images, but these approaches are typically optimized for pixel-wise RAW reconstruction fidelity and tend to degrade under diverse rendering styles and editing operations. We introduce a plug-and-play, edit-aware loss function that can be integrated into any existing RAW reconstruction framework to make the recovered RAWs more robust to different rendering styles and edits. Our loss formulation incorporates a modular, differentiable image signal processor (ISP) that simulates realistic photofinishing pipelines with tunable parameters. During training, parameters for each ISP module are randomly sampled from carefully designed distributions that model practical variations in real camera processing. The loss is then computed in sRGB space between ground-truth and reconstructed RAWs rendered through this differentiable ISP. Incorporating our loss improves sRGB reconstruction quality by up to 1.5-2 dB PSNR across various editing conditions. Moreover, when applied to metadata-assisted RAW reconstruction methods, our approach enables fine-tuning for target edits, yielding further gains. Since photographic editing is the primary motivation for RAW reconstruction in consumer imaging, our simple yet effective loss function provides a general mechanism for enhancing edit fidelity and rendering flexibility across existing methods.

Reconstructing photorealistic and animatable 4D head avatars from a single portrait image remains a fundamental challenge in computer vision. While diffusion models have enabled remarkable progress in image and video generation for avatar reconstruction, existing methods primarily rely on 2D priors and struggle to achieve consistent 3D geometry. We propose a novel framework that leverages geometry-aware diffusion to distill strong geometry priors for high-fidelity head avatar reconstruction. Our approach jointly synthesizes portrait images and corresponding surface normals, while a pose-free expression encoder captures implicit expression representations. Both synthesized images and expression latents are distilled into 3D Gaussian-based avatars, enabling photorealistic rendering with accurate geometry. Extensive experiments demonstrate that our method substantially outperforms state-of-the-art approaches in visual quality, expression fidelity, and cross-identity generalization, while supporting real-time rendering.

Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space, which is critical for applications like drone navigation and security surveillance. However, it faces two key bottlenecks in real-world deployment: confusion from visually similar distractors caused by insufficient instance-level discrimination and severe failure under occlusions due to the absence of active planning. To address these, we propose OA-VAT, a unified pipeline with three complementary modules. First, a training-free Instance-Aware Offline Prototype Initialization aggregates multi-view augmented features via DINOv3 to construct discriminative instance prototypes, mitigating distractor confusion. Second, an Online Prototype Enhancement Tracker enhances prototypes online and integrates a confidence-aware Kalman filter for stable tracking under appearance and motion changes. Third, an Occlusion-Aware Trajectory Planner, trained on our new Planning-20k dataset, uses conditional diffusion to generate obstacle-avoiding paths for occlusion recovery. Experiments demonstrate OA-VAT achieves 0.93 average SR on UnrealCV (+2.2% vs. SOTA TrackVLA), 90.8% average CAR on real-world datasets (+12.1% vs. SOTA GC-VAT), and 81.6% TSR on a DJI Tello drone. Running at 35 FPS on an RTX 3090, it delivers robust, real-time performance for practical deployment. The code is available at https://github.com/SHWplus/OA-VAT.

Recent advancements in remote sensing object detection have predominantly focused on oriented bounding box design and small object feature enhancement, while often overlooking the intrinsic geometric properties of remote sensing images, such as rotation invariance and structural symmetry. Many aerial objects appear in multiple orientations and exhibit clear symmetrical patterns, which, if not explicitly modeled, can lead to detection failures and inaccurate localization under geometric variation or partial occlusion. To address this, we propose the Rotation Invariant and Symmetry Aware Pixel Difference Network (RIS-PiDiNet), which introduces a novel convolutional operator called Rotation Invariant and Symmetry Aware Pixel Difference Convolution (RIS-PDC). This operator replaces traditional convolution with a mathematically grounded formulation that encodes rotation group priors and symmetrical constraints. RIS-PDC utilizes pixel differences and symmetry-guided aggregation in the polar harmonic space, enabling the network to infer partially visible structures and deduce occluded symmetrical parts. Besides improving detection accuracy, RIS-PDC enhances model interpretability by embedding geometric principles into the network design. Feature visualizations demonstrate rotation-consistent activations and symmetry-complete responses, revealing how the network captures underlying object structure even under partial visibility or orientation changes. This yields geometrically interpretable detection decisions. To our knowledge, RIS-PiDiNet is the first remote sensing object detection framework that jointly incorporates rotation invariance and symmetry modeling within a unified architecture. Extensive evaluations on standard benchmarks validate its effectiveness, achieving state-of-the-art performance on DOTA-v1.0 (78.53% mAP single-scale, 81.81% multi-scale), HRSC2016 (98.60% mAP), and DIOR-R (67.28% mAP), all with acceptable computational overhead and no increase in parameter count.

Modern detectors typically deepen backbones and rely on aggressive downsampling to harvest high-level semantics. But this severely degrades low-energy infrared tiny targets via rescale-induced information loss. This work introduces InvDet, a target-aware invertible encoder that unifies information preservation and target-aware enhancement within a reconstruction-guided detection framework. An invertible pathway reconstructs the input from feature latents, exposing information loss as an optimizable quantity. To decouple detection from irrelevant reconstruction, a Target-Aware Reconstruction Modulation (TARM) module operates only in the inverse path, gating high-pass latents and applying a mild gain to low-pass features without altering the forward detection distribution. In addition, a Geometry-Content Tolerance Metric (GCTM) is proposed to focus on truly informative regions and yields a pixel-wise weight map that gently regularizes the reconstruction branch. Our method achieves competitive accuracy on five public infrared benchmarks while exhibiting strong cross-dataset generalization, providing a principled pathway toward detection-friendly representation learning for scale-challenged visual regimes.

Multimodal Emotion Recognition in Conversations (MERC) aims to understand emotions expressed in each utterance by effectively integrating audio, text, and visual modalities. However, in real-world scenarios, unavoidable missing modalities often degrade multimodal interpretation performance. To address this, we propose Hypergraph Diffusion and Evidence Fusion based Emotion Recognition (HyperEF), a novel framework designed to mitigate challenges arising from incomplete modalities in MERC. Specifically, to mitigate performance degradation caused by modality absence, we propose Masked Hypergraph Attention (MHGAT) conditioned diffusion model to recover latent features of missing modalities in the latent space. To ensure semantic consistency between recovered and available modalities within the same utterance, we introduce MHGAT that captures high-order semantic information from available modalities to guide the diffusion model's denoising process. Furthermore, to disentangle and model the complex uncertainties inherent in MERC, we propose Dual Channel Evidence Fusion (DCEF), which estimates uncertainty at both feature source level and discriminative level, thereby achieving adaptive evidence fusion. Extensive comparative experiments and interpretability demonstrate the superior performance of our model in emotion recognition, as well as the contribution of each module within the model.

Recent advances in image editing allow impressive manipulation of objects, existing methods still struggle to handle spatial movement in complex scenes, such as objects span different depth layers or are partially occluded. Most image editing methods focus solely on prior information from 2D datasets, emphasizing planar features while lacking support for spatial structures. Even approaches that incorporate explicit positional information fail to capture true 3D spatial relationships, thus limiting accurate object movement in complex scenes. In this paper, we present SpatialDiff, a method that effectively captures 3D spatial structures, enabling precise and consistent object movements in complex scenes. Our core innovations are twofold: (1) Implicit 3D Spatial Modeling, which introduces 3D prior knowledge and enables the model to internally build a comprehensive understanding of the three-dimensional spatial structure; and (2) Global Spatial Supervision, which constrains the latent spatial features to enable the model to perceive changes in object spatial positions caused by editing operations. Experimental results demonstrate that our method significantly improves the accuracy and fidelity of spatial movement in complex scenes.

Partial shape matching is a crucial yet underexplored problem in 3D vision, with significant relevance to real-world scenarios where shapes are often only partially observed. Existing feature descriptors face difficulties in this setting, as traditional representations either struggle with the boundaries of partial shapes or heavily depend on the shape's spatial position. While existing approaches have employed DINO features for partial shape matching, these features are not inherently suited for handling partial observations. In this work, we propose a method to refine DINO features using LoRA-based self-supervised learning, enabling the generation of feature descriptors that are robust to partiality. Our features substantially improve performance on partial shape matching compared to traditional or vision foundation features. Additionally, when integrated into existing partial shape matching pipelines, we achieve state-of-the-art results on partial shape matching and left-right prediction benchmarks.

Reconstructing high-fidelity, animatable human avatars from monocular videos remains a critical challenge. Existing 3DGS-based human animation methods constrain Gaussian parameters but exclude scale, which we argue is crucial for adapting human poses to challenging out-of-distribution poses. To achieve robust animation under unseen poses, we propose Tavatar, which derives key parameters such as scale, rotation, and other geometric attributes directly from the local mesh geometry, instead of learning them through unconstrained optimization. This paradigm shift enforces topological consistency by design, as each Gaussian is analytically anchored to the local mesh geometry, inheriting its spatial structure and deformation behavior. Specifically, we bind Gaussians to mesh faces and vertices, deriving their scales and orientations from triangle properties and local edge lengths to ensure coherent surface coverage. To ensure the stability of this analytical mapping, we introduce a crucial equilateral regularization term that preserves mesh integrity. Extensive experiments demonstrate that Tavatar achieves superior animation robustness on challenging out-of-distribution poses, reducing normal error by 13.8% on X-Avatar and 17.9% on PeopleSnapshot against the best baseline, while maintaining competitive rendering quality.

Novel Class Discovery in Point Cloud Segmentation is recently proposed, aiming to leverage knowledge from known classes to automatically segment unlabeled classes within point clouds. The core of this task lies in leveraging the geometric and semantic knowledge of multiple known classes to achieve semantic understanding and segmentation of novel classes.However, existing methods overlook the high-order associations between known and novel classes, relying solely on binary associations for class assignment and novel class reasoning, which leads to less precise semantic segmentation.To address these issues, we introduce a hypergraph structure to model high-order associations among classes, enabling collaborative reasoning from known classes to novel classes, extending beyond traditional binary relations.Additionally, existing methods focus excessively on extracting semantic information when processing point cloud data, neglecting the importance of geometric features. To address this, we introduce Geometric-Aware Prototypes, enhancing the model's ability to capture geometric spatial information.By propagating geometric information through hyperedges, our method enhances the understanding of spatial distributions across classes, improving segmentation accuracy.Significant performance improvements achieved on the SemanticKITTI and SemanticPOSS datasets demonstrate the superiority of our method.

Online High-Definition (HD) map construction is pivotal for autonomous driving. While recent approaches leverage historical temporal fusion to improve performance, we identify a critical safety flaw in this paradigm: it is inherently "spatially backward-looking." These methods predominantly enhance map reconstruction in traversed areas, offering minimal improvement for the unseen road ahead. Crucially, our analysis of downstream planning tasks reveals a severe asymmetry: while rearward perception errors are often tolerable, inaccuracies in the forward region directly precipitate hazardous driving maneuvers. To bridge this safety gap, we propose AMap, a novel framework for Ahead-aware online HD Mapping. We pioneer a "distill-from-future" paradigm, where a teacher model with privileged access to future temporal contexts guides a lightweight student model restricted to the current frame. This process implicitly compresses prospective knowledge into the student model, endowing it with "look-ahead" capabilities at zero inference-time cost. Technically, we introduce a Multi-Level BEV Distillation strategy with spatial masking and an Asymmetric Query Adaptation module to effectively transfer future-aware representations to the student's static queries. Extensive experiments on the nuScenes and Argoverse 2 benchmark demonstrate that AMap significantly enhances current-frame perception. Most notably, it outperforms state-of-the-art temporal models in critical forward regions while maintaining the efficiency of current-frame inference.

3D Gaussian splatting (3DGS) has demonstrated impressive performance in synthesizing high-fidelity novel views. Nonetheless, its effectiveness critically depends on the quality of the initialized point cloud. Specifically, achieving uniform and complete point coverage over the underlying scene structure requires overlapping observation frustums, an assumption that is often violated in unbounded, dynamic urban environments. Training Gaussian models with partially initialized point clouds often leads to distortions and artifacts, as camera rays may fail to intersect valid surfaces, resulting in incorrect gradient propagation to Gaussian primitives associated with occluded or invisible geometry. Additionally, existing densification strategies simply clone and split Gaussian primitives from existing ones, incapable of reconstructing geometry from missing structures. To address these limitations, we propose VAD-GS, a 3DGS framework tailored for geometry recovery in challenging urban scenes. Our method identifies unreliable geometry structures via voxel-based visibility reasoning, selects informative supporting views through diversity-aware view selection, and recovers missing structures via multi-view stereo reconstruction. This design enables the generation of new Gaussian primitives guided by reliable geometric priors, even in regions lacking initial points. Extensive experiments on the Waymo and nuScenes datasets demonstrate that VAD-GS outperforms state-of-the-art 3DGS approaches and significantly improves the quality of reconstructed geometry for both static and dynamic objects.Source code will be released upon publication.

Current diffusion-based makeup transfer methods commonly use the makeup information encoded by off-the-shelf foundation models (e.g., CLIP) as condition to preserve the makeup style of reference image in the generation. Although effective, these works mainly have two limitations: (1) foundation models pre-trained for generic tasks struggle to capture makeup styles; (2) the makeup features of reference image are injected to the diffusion denoising model as a whole for global makeup transfer, overlooking the facial region-aware makeup features (i.e., eyes, mouth, etc) and limiting the regional controllability for region-specific makeup transfer. To address these, in this work, we propose Facial Region-Aware Makeup features (FRAM), which has two stages: (1) makeup CLIP fine-tuning; (2) identity and facial region-aware makeup injection. For makeup CLIP fine-tuning, unlike prior works using off-the-shelf CLIP, we synthesize annotated makeup style data using GPT-o3 and text-driven image editing model, and then use the data to train a makeup CLIP encoder through self-supervised and image-text contrastive learning. For identity and facial region-aware makeup injection, we construct before-and-after makeup image pairs from the edited images in stage 1 and then use them to learn to inject identity of source image and makeup of reference image to the diffusion denoising model for makeup transfer. Specifically, we use learnable tokens to query the makeup CLIP encoder to extract facial region-aware makeup features for makeup injection, which is learned via an attention loss to enable regional control. As for identity injection, we use a ControlNet Union to encode source image and its 3D mesh simultaneously. The experimental results verify the superiority of our regional controllability and our makeup transfer performance.

Gaussian Splatting has emerged as a popular 3D representation technique, but still struggles with appearance inconsistencies, especially in dark scenes that require active illumination (e.g., camera flashes or co-moving light sources) to capture usable images, leading to dramatic local appearance fluctuations.While existing methods mainly focus on modeling global appearance changes for in-the-wild scenes, such as those caused by different times of day or weather conditions, they fail to handle the severe variations present in dark scenes with moving light sources.In this paper, we propose a novel Gaussian Splatting-based approach for constructing scene representations in dark scenes where active light sources are rigidly attached to the camera and move together with it.Within this framework, we introduce an illumination-weighted loss function that drives the representation toward the underlying unlit scene. Furthermore, instead of adjusting the illumination of each individual Gaussian as in prior work, we employ a tile-based shading scheme that operates directly on the rendered images, greatly reducing computational cost while explicitly separating illumination from intrinsic scene appearance.Additionally, we further refine the learned Gaussian representation by combining the recovered unlit scene appearance with an advanced geometric prior model, which significantly improves geometric accuracy.Experimental results demonstrate that our method achieves superior reconstruction quality in challenging environments compared to state-of-the-art techniques.

Incremental learning (IL) arises from the need to continuously update models under limited data and computational resources. Most existing IL studies focus on data-scarce settings. They often develop complex methods that rely on heavy computation, while overlooking the computational resource constraints common in real-world scenarios. This motivates us to formalize the problem of Computational Resource-Aware Incremental Learning, which explicitly considers the computational budget during model training. To tackle this problem, we propose Smart Replay, an efficient memory rehearsal algorithm that adaptively allocates resources by scheduling the replay ratio across mini-batches. We cast replay-ratio optimization into an optimal control formulation that jointly minimizes new-task and memory losses. We further propose a heuristic Q-function to guide ratio adjustments, adaptively balancing short-term efficiency and long-term stability. Finally, we develop a practical algorithm that periodically updates the replay ratio during training. Experiments on multiple benchmarks validate that Smart Replay consistently outperforms fixed-replay baselines, achieving higher accuracy and lower forgetting under the same computational budget.

Although current watermarking techniques for 3D Gaussian Splatting (3DGS) are promising in protecting the copyrights of both 3DGS models and their rendered images, they greatly suffer from low watermark robustness and poor rendering quality when applying quantization to large 3DGS models to accommodate resource-limited devices. To address these problems, this paper introduces a novel two-stage quantization-aware 3DGS watermarking approach called Robust3DGSW. By properly embedding watermarks into the mid-frequency bands of both the 3D Gaussian parameters and 2D rendered images, the first stage of Robust3DGSW can effectively counteract the quantization-induced signal loss and mitigate the adverse effects of watermarks on rendered images. In the second stage, Robust3DGSW trains both 2D and 3D decoders using our proposed multi-scale adversarial perturbation approach, alongside a gradual quantization process, which enables robust watermark extraction even under excessive quantization. Comprehensive experimental results obtained from the well-known Blender, LLFF, and MipNeRF-360 datasets demonstrate that, when compared to leading 3DGS watermarking techniques, Robust3DGSW not only mitigates the negative effects of quantization on watermarks but also enables fast rendering with high quality.

Inter-object relations underpin spatial intelligence, yet existing representations--linguistic prepositions or object-level scene graphs--are too coarse to specify which regions actually support, contain, or contact one another, leading to ambiguous and physically inconsistent layouts. To address these ambiguities, a part-level formulation is needed; therefore, we introduce PARSE, a framework that explicitly models how object parts interact to determine feasible and spatially grounded scene configurations. PARSE centers on the Part-centric Assembly Graph (PAG), which encodes geometric relations between specific object parts, and a Part-Aware Spatial Configuration Solver that converts these relations into geometric constraints to assemble collision-free, physically valid scenes. Using PARSE, we build PARSE-10K, a dataset of 10,000 3D indoor scenes constructed from real-image layout priors and a curated part-annotated shape database, each with dense contact structures and a part-level contact graph. With this structured, spatially grounded supervision, fine-tuning Qwen3-VL on PARSE-10K yields stronger object-level layout reasoning and more accurate part-level relation understanding; furthermore, leveraging PAGs as structural priors in 3D generation models leads to scenes with substantially improved physical realism and structural complexity. Together, these results show that PARSE significantly advances geometry-grounded spatial reasoning and supports the generation of physically consistent 3D scenes.