Text-to-image diffusion models have achieved remarkable progress, generating visually realistic and semantically coherent images from textual prompts. However, natural language alone lacks the precision required for design-centric applications that demand strict spatial and structural fidelity--particularly when representing complex concepts that integrate multi-level information, such as product or scene design. To address this limitation, controllable diffusion frameworks introduce auxiliary conditions (e.g., depth, edge, or reference images) to guide the generative process. Models like ControlNet and IP-Adapter effectively inject such priors, improving structural or appearance alignment. Yet, real-world design tasks rarely depend on a single type of condition. They often require simultaneous integration of multiple heterogeneous cues--for instance, preserving spatial layout from depth maps, structural outlines from edge maps, and stylistic attributes from reference images. Current approaches either handle only one condition or naively stack multiple ones, resulting in computational inefficiency and conflicting guidance that degrade generation quality. This multi-condition inconsistency forms a critical bottleneck for applying diffusion models to real-world design workflows, motivating our proposed framework. We propose a data-driven adaptive condition fusion mechanism for multi-conditional diffusion. Our method introduces a novel condition adaptation module that dynamically selects and fuses subsets of conditions based on the diffusion timestep, task characteristics, and feature injection position. This adaptive strategy harmonizes diverse structural and appearance priors, achieving controllable yet flexible generation in complex design scenarios. Experiments demonstrate significant improvements in fidelity, consistency, and controllability across multi-condition tasks, establishing a new direction for practical, detail-preserving diffusion-based design generation.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (TAP), a training-free, probe-driven framework that adaptively selects a predictor for each token at every sampling step. TAP uses a single full evaluation of the model's first layer as a low-cost probe to compute proxy losses for a compact family of candidate predictors (instantiated primarily with Taylor expansions of varying order and horizon), then assigns each token the predictor with the smallest proxy error. This per-token "probe-then-select" strategy exploits heterogeneous temporal dynamics, requires no additional training, and is compatible with various predictor designs. TAP incurs negligible overhead while enabling large speedups with little or no perceptual quality loss. Extensive experiments across multiple diffusion architectures and generation tasks show that TAP substantially improves the accuracy-efficiency frontier compared to fixed global predictors and caching-only baselines.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we present MM-ACT, a unified Vision-Language-Action (VLA) model that integrates text, image, and action in shared token space and performs generation across all three modalities. MM-ACT adopts a re-mask parallel decoding strategy for text and image generation, and employs a one-step parallel decoding strategy for action generation to improve efficiency. We introduce Context-Shared Multimodal Learning, a unified training paradigm that supervises generation in all three modalities from a shared context, enhancing action generation through cross-modal learning. Experiments were conducted on the LIBERO simulation and Franka real-robot setups as well as RoboTwin2.0 to assess in-domain and out-of-domain performances respectively. Our approach achieves a success rate of 96.3% on LIBERO, 72.0% across three tasks of real Franka, and 52.38% across eight bimanual tasks of RoboTwin2.0 with an additional gain of 9.25% from cross-modal learning. We release our codes, models and data at https://github.com/HHYHRHY/MM-ACT.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning-based methods are trained on image pairs, a paradigm that fails to explicitly optimize for the long-term trackability of keypoints across sequences under challenging viewpoint and illumination changes. In this paper, we reframe keypoint detection as a sequential decision-making problem. We introduce TraqPoint, a novel, end-to-end Reinforcement Learning (RL) framework designed to optimize the Track-quality (Traq) of keypoints directly on image sequences. Our core innovation is a track-aware reward mechanism that jointly encourages the consistency and distinctiveness of keypoints across multiple views, guided by a policy gradient method. Extensive evaluations on sparse matching benchmarks, including relative pose estimation and 3D reconstruction, demonstrate that TraqPoint significantly outperforms some state-of-the-art keypoint detection and description methods. The code will be available at https://github.com/xiaomi-research/traqpoint.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Recent advances in text-to-image (T2I) generation can produce highly resembling images of copyrighted characters, often indistinguishable from official depictions, raising serious concerns about intellectual property infringement. Consequently, robust detection of copyright character infringement is urgently needed. Yet, existing methods exhibit limited alignment with human judgments regarding the likelihood of infringement. To bridge this gap, we propose CopyLens, a novel prompt optimization framework that automatically refines textual prompts for vision-language model-based detectors to better match human infringement judgments. Our approach establishes a closed-loop refinement process between a large vision-language model (LVLM) and a large language model (LLM): the LVLM assesses generated images for copyright detection, while the LLM iteratively optimizes detection prompts via meta-prompting, guided by feedback signals derived from human annotation consistency. To facilitate the assessment of prompt-human alignment, we introduce CopyChars, a new large-scale dataset of over 7,000 AI-generated images spanning more than 100 popular copyrighted characters, along with detailed human annotations on potential infringement. Extensive experiments on CopyChars show that the proposed CopyLens can improve detection performance by 5% to 10% compared to recent state-of-the-art methods. This work offers a scalable and automated solution for visual copyright protection and highlights the critical role of prompt engineering.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Dynamic driving scene modeling is critical for autonomous driving simulation and closed-loop learning. While recent feed-forward methods offer fast inference through data-driven priors, they struggle with long-range driving sequences due to quadratic complexity in sequence length and restrictive assumptions for dynamic objects. We propose UFO, a novel recurrent paradigm that combines the strengths of optimization-based and feed-forward methods for efficient long-range 4D modeling. Our approach maintains a persistent 4D scene representation composed of scene tokens that are progressively refined as new frames arrive, enabling future observations to correct earlier uncertain predictions. A visibility-based filtering mechanism exploits the locality of 3D-to-pixel correspondence, reducing complexity from quadratic to near-linear in sequence length. For dynamic objects, we introduce object pose-guided modeling with learned lifespans, enabling complex long-range motion without kinematic assumptions. Experiments on the Waymo Open Dataset demonstrate that UFO significantly outperforms both per-scene optimization and feed-forward baselines across 2s, 8s, and zero-shot 16s sequences.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Table recognition (TR) aims to transform table images into semi-structured representations such as HTML or Markdown.As a core component of document parsing, TR has long relied on supervised learning, with recent efforts dominated by fine-tuning vision-language models (VLMs) using labeled data.While VLMs have brought TR to the next level, pushing performance further demands large-scale labeled data that is costly to obtain.Consequently, although proprietary models have continuously pushed the performance boundary, open-source models, often trained with limited resources and, in practice, the only viable option for many due to privacy regulations, still lag far behind.To bridge this gap, we introduce TRivia, a self-supervised fine-tuning method that enables pretrained VLMs to learn TR directly from unlabeled table images in the wild. Built upon Group Relative Policy Optimization, TRivia automatically identifies unlabeled samples that most effectively facilitate learning and eliminates the need for human annotations through a question-answering-based reward mechanism. An attention-guided module generates diverse questions for each table image, and the ability to interpret the recognition results and answer them correctly provides feedback to optimize the TR model.This closed-loop process allows the TR model to autonomously learn to recognize, structure, and reason over tables without labeled data. Leveraging this pipeline, we present TRivia-3B, an open-sourced, compact, and state-of-the-art TR model that surpasses existing systems (e.g., Gemini 2.5 Pro, MinerU2.5) on three popular benchmarks.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Vision-and-Language Navigation (VLN) requires agents to follow long-horizon instructions and navigate complex 3D environments. However, existing approaches face two major challenges: constructing an effective long-term memory bank and overcoming the compounding errors problem. To address these issues, we propose DecoVLN, an effective framework designed for robust streaming perception and closed-loop control in long-horizon navigation. First, we formulate long-term memory construction as an optimization problem and introduce adaptive refinement mechanism that selects frames from a historical candidate pool by iteratively optimizing a unified scoring function. This function jointly balances three key criteria: semantic relevance to the instruction, visual diversity from the selected memory, and temporal coverage of the historical trajectory. Second, to alleviate compounding errors, we introduce a state-action pair-level corrective finetuning strategy. By leveraging geodesic distance between states to precisely quantify deviation from the expert trajectory, the agent collects high-quality state-action pairs in the trusted region while filtering out the polluted data with low relevance. This improves both the efficiency and stability of error correction. Extensive experiments demonstrate the effectiveness of DecoVLN, and we have deployed it in real-world environments.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to use tools for verification, limiting their reliability on complex multimodal reasoning tasks.We present **ARM-Thinker**, an **A**gentic multimodal **R**eward **M**odel that autonomously invokes external tools (e.g., image cropping, doc page retrieval) to ground judgments in verifiable evidence, replacing static, non-interactive reward scoring.This enables the model to verify fine-grained visual details, cross-reference multi-page evidence, and validate reasoning claims, which are capabilities absent in existing reward models.We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy.To evaluate agentic reward modeling, we introduce **ARMBench-VL**, comprising three benchmarks that assess fine-grained visual grounding (image-level tools), multi-page document understanding (retrieval tools), and instruction following (text-level verification).ARM-Thinker achieves +16.2% average improvement on reward modeling benchmarks, +9.6% on tool-use tasks, and outperforms baselines on multimodal math and logical reasoning benchmarks.Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.
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cs.CV · cs.AI
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Diffusion Transformers (DiTs) have greatly advanced text-to-image generation, but models still struggle to generate the correct spatial relations between objects as specified in the text prompt. In this study, we adopt a mechanistic interpretability approach to investigate how a DiT can generate correct spatial relations between objects. We train, from scratch, DiTs of different sizes with different text encoders to learn to generate images containing two objects whose attributes and spatial relations are specified in the text prompt. We find that, although all the models can learn this task to near-perfect accuracy, the underlying mechanisms differ drastically depending on the choice of text encoder. When using random text embeddings, we find that the spatial-relation information is passed to image tokens through a two-stage circuit, involving two cross-attention heads that separately read the spatial relation and single-object attributes in the text prompt. When using a pretrained text encoder (T5), we find that the DiT uses a different circuit that leverages information fusion in the text tokens, reading spatial-relation and single-object information together from a single text token. We further show that, although the in-domain performance is similar for the two settings, their robustness to out-of-domain perturbations differs, potentially suggesting the difficulty of generating correct relations in real-world scenarios.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Self-attention with separate pre- and post-projections can be a universal approximator (on compact domains) under mild conditions. Yet we observe a striking gap: an attention-only Transformer (w/o FFN layers) exhibits a marked accuracy drop relative to its standard interleaved attention--FFN baseline. We term this the weak-independence challenge of attention. We study this through a new conceptual lens, Selection-as-Nonlinearity (SaN), which interprets effective nonlinearity as directed, cost-constrained selection, offering a coherent account of attention as context-gated activation. In this joint game-decision view, attention performs a resource-constrained cooperative allocation over values: each query distributes a unit-mass weight budget over shared values to optimize representational utility, under a normalizer (e.g., \mathrm softmax ), and guided by context-derived scores (e.g., q-k similarities). SaN interprets weak-independence as a structural tension: the value weights almost cannot simultaneously attain the decoupled per-query (row-wise) and the per-value (column-wise) optimums under shared budgets, thereby limiting attention's stand-alone capacity. Guided by SaN, we introduce CSaN, an interpretable, efficient attention compensation paradigm with two key insights: 1) hierarchical budget calibration, re-allocate row budgets via inter-query correction signals; and 2) public-private cooperation, enhancing the public attention pathway with a per-token private value pathway to decouple conflicting demands. CSaN is evaluated on various vision benchmarks and demonstrates remarkable gains across popular Transformer families (Swin, ViT, Hiera), enabling models to rival much heavier same-family counterparts ~2xas large.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
The critical challenge of semi-supervised semantic segmentation lies in how to fully exploit a large volume of unlabeled data to improve the model's generalization performance for robust segmentation. However, existing softmax scores-based filtering methods tend to be affected by the overconfidence issue in neural networks, leading to the inclusion of incorrect pseudo-labels that negatively impact the training process. In this paper, we propose a novel evidential learning framework to explicitly model the prediction uncertainty for reliable pseudo-label selection. By modeling the distribution of class probabilities using Dirichlet distributions, we obtain principled and improved uncertainty estimates from a distributional perspective. Furthermore, we propose HESS (Hyper-ESS), decoupling the modeling of exclusive and collective evidence for comprehensive evidence perception, to yield more accurate uncertainty estimates. Extensive experiments on three challenging benchmarks demonstrate that integrating HESS into existing semi-supervised semantic segmentation frameworks consistently improves performance, benefiting from more reliable pseudo-label selection. Our work sheds light on the potential of evidential learning in semi-supervised semantic segmentation and opens up new avenues for future research.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike existing datasets limited to merely hundreds or thousands of examples, PhysInOne provides 2 million videos across 153,810 dynamic 3D scenes, covering 71 basic physical phenomena in mechanics, optics, fluid dynamics, and magnetism. Distinct from previous works, our scenes feature multiobject interactions against complex backgrounds, with comprehensive ground-truth annotations including 3D geometry, semantics, dynamic motion, physical properties, and text descriptions. We demonstrate PhysInOne's efficacy across four emerging applications: physics-aware video generation, long-/short-term future frame prediction, physical property estimation, and motion transfer. Experiments show that fine-tuning foundation models on PhysInOne significantly enhances physical plausibility, while also exposing critical gaps in modeling complex physical dynamics and estimating intrinsic properties. As the largest dataset of its kind, orders of magnitude beyond prior works, PhysInOne establishes a new benchmark for advancing physics-grounded world models in generation, simulation, and embodied AI.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Look-Up Table based methods have emerged as a promising direction for efficient image restoration tasks. Recent LUT-based methods focus on improving their performance by expanding the receptive field. However, they inevitably introduce extra computational and storage overhead, which hinders their deployment in edge devices.To address this issue, we propose ShiftLUT, a novel framework that attains the largest receptive field among all LUT-based methods while maintaining high efficiency. Our key insight lies in three complementary components. First, Learnable Spatial Shift module (LSS) is introduced to expand the receptive field by applying learnable, channel-wise spatial offsets on feature maps. Second, we propose an asymmetric dual-branch architecture that allocates more computation to the information-dense branch, substantially reducing inference latency without compromising restoration quality.Finally, we incorporate a feature-level LUT compression strategy called Error-bounded Adaptive Sampling (EAS) to minimize the storage overhead.Compared to the previous state-of-the-art method TinyLUT, ShiftLUT achieves a 3.8xlarger receptive field and improves an average PSNR by over 0.21 dB across multiple standard benchmarks, while maintaining a small storage size and inference time.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Wei, Xiaobao, Ye, Zhangjie, Gu, Yuxiang, Zhu, Zunjie, Guo, Yunfei, Shen, Yingying, Zhao, Shan, Lu, Ming 等 13 位作者
Parking is a critical task for autonomous driving systems (ADS), with unique challenges in crowded parking slots and GPS-denied environments. However, existing works focus on 2D parking slot perception, mapping, and localization, 3D reconstruction remains underexplored, which is crucial for capturing complex spatial geometry in parking scenarios. Naively improving the visual quality of reconstructed parking scenes does not directly benefit autonomous parking, as the key entry point for parking is the slots perception module. To address these limitations, we curate the first benchmark named ParkRecon3D, specifically designed for parking scene reconstruction. It includes sensor data from four surround-view fisheye cameras with calibrated extrinsics and dense parking slot annotations. We then propose ParkGaussian, the first framework that integrates 3D Gaussian Splatting (3DGS) for parking scene reconstruction. To further improve the alignment between reconstruction and downstream parking slot detection, we introduce a slot-aware reconstruction strategy that leverages existing parking perception methods to enhance the synthesis quality of slot regions. Experiments on ParkRecon3D demonstrate that ParkGaussian achieves state-of-the-art reconstruction quality and better preserves perception consistency for downstream tasks. The code and dataset will be released.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Recent advancements in reasoning language models have fueled growing interest in extending such capabilities to multimodal domains. However, despite notable progress in visual and video reasoning, the lack of transparent and reproducible data curation and training pipelines remains a major barrier to scalable research. In this work, we introduce OpenMMReasoner, a fully transparent two-stage recipe for multimodal reasoning spanning supervised fine-tuning (SFT) and reinforcement learning (RL). In the SFT stage, we construct an 874k-sample cold-start dataset with rigorous step-by-step validation, providing a strong foundation for reasoning capabilities. The subsequent RL stage leverages a 74k-sample dataset across diverse domains to further sharpen and stabilize these abilities, resulting in a more robust and efficient learning process. Extensive evaluations demonstrate that our training recipe not only surpasses strong baselines but also highlights the critical role of data quality and training design in shaping multimodal reasoning performance. Notably, our method achieves a 9.5% improvement over the Qwen2.5-VL-7B-Instruct baseline across nine multimodal reasoning benchmarks, establishing a solid empirical foundation for future large-scale multimodal reasoning research.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Document AI has advanced rapidly and is attracting increasing attention. Yet, while most efforts have focused on document layout analysis (DLA), its generative counterpart, layout generation, remains underexplored. Distinct from traditional graphic layout design and room layout planning, document layout generation typically involves a larger number of elements per page and exhibits greater structural diversity and complexity. Currently, a major obstacle lies in the scarcity of diverse document layouts: academic papers with Manhattan-style structures dominate existing studies, while open-world genres such as newspapers and magazines remain severely underrepresented. To address this gap, we curate OmniDocLayout-1M, the first million-scale dataset of diverse document layouts, covering six common document types and comprising contemporary layouts collected from multiple sources. Moreover, since existing methods struggle in complex domains and often fail to arrange long sequences coherently, we introduce OmniDocLayout-LLM, a 0.5B model with designed two-stage Coarse-to-Fine learning paradigm: 1) learning universal layout principles from our dataset with coarse category definitions, and 2) transferring the knowledge to a specific domain with few fine-grained annotated samples. Extensive experiments demonstrate that our approach achieves strong performance on multiple domains in M^6Doc dataset, substantially surpassing both existing layout generation experts and several latest general-purpose LLMs. More information can be found at https://github.com/opendatalab/OmniDocLayout.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approaches relegate world models to limited roles: they operate within ostensibly unified architectures that still keep world prediction and motion planning as decoupled processes. To bridge this gap, we propose DriveLaW, a novel paradigm that unifies video generation and motion planning. By directly injecting the latent representation from its video generator into the planner, DriveLaW ensures inherent consistency between high-fidelity future generation and reliable trajectory planning. Specifically, DriveLaW consists of two core components: DriveLaW-Video, our powerful world model that generates high-fidelity forecasting with expressive latent representations, and DriveLaW-Act, a diffusion planner that generates consistent and reliable trajectories from the latent of DriveLaW-Video, with both components optimized by a three-stage progressive training strategy. New state-of-the-art results across both tasks demonstrate the power of our unified paradigm. DriveLaW not only significantly advances video prediction, surpassing the previous best-performing work by 33.3% in FID and 1.8% in FVD, but also sets a new record on the NAVSIM planning benchmark.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
This paper introduces UniMERNet, a high-accuracy, computation-efficient algorithm for Mathematical Expression Recognition (MER) across diverse real-world scenarios. To facilitate UniMERNet's training, we constructed UniMER-1M, a million-scale dataset whose unprecedented diversity endows the model with robust generalization ability. Through in-depth analysis, we discover a distinctive raster-scan pattern (left-to-right, top-to-bottom) in the attention distribution of Transformer models for MER tasks, which closely aligns with human reading habits. Based on this key finding, we design an innovative **Raster-Scan Attention** mechanism that employs a "horizontal-first, vertical-second" sequential attention computation strategy. This approach not only successfully reduces computational complexity from \mathcal O (NH^2W^2D) to \mathcal O (NHWD(H + W)) , but also enables the model to capture long-range dependencies more efficiently, achieving recognition performance comparable to global attention. Leveraging both UniMER-1M and our innovative attention mechanism, UniMERNet achieves state-of-the-art performance across four real-world scenarios while significantly reducing computational resources compared to global attention: over **1.2x** memory savings during training, approximately **10x** memory reduction during inference, and **5x** speed with slightly improved accuracy. All resources will be publicly released to advance MER research further.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Dark optical flow estimation (DOFE) faces critical challenges: discriminative models are less robust to noise and struggle with weakened motion patterns, while diffusion models suffer from discontinuous flow fields and low efficiency. Flow matching (FM), though efficient, remains underexplored for conditional generation in DOFE. In this paper, we propose FlowFM, the first flow matching model tailored to DOFE tasks. Instead of conventional vector field regression, FlowFM proposes estimating the global transformation path constrained by the ground truth optical flow. It generates noisy flow by mixing Gaussian noise with ground truth, then performs a one-step denoising process conditioned on the initial flow field, cost volume, and contextual features for optimal accuracy and efficiency. FlowFM incorporates an implicit Fourier denoising decoder (IFDD) for reliable motion understanding. By leveraging the Fourier transform, IFDD uses amplitude to characterize motion intensity and phase to encode target spatial relationships within flow fields, then directly enhances amplitude to restore dark-caused motion information loss. Experiments show that FlowFM significantly outperforms state-of-the-art methods on the FCDN and VBOF benchmarks, setting a new performance record for DOFE.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
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.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表