CVPR 2026 Papers

Identity-preserving video generation offers powerful tools for creative expression, allowing users to customize videos featuring their beloved characters. However, prevailing methods are typically designed and optimized for a single identity reference. This underlying assumption restricts creative flexibility by inadequately accommodating diverse real-world input formats. Relying on a single source also constitutes an ill-posed scenario, causing an inherently ambiguous setting that makes it difficult for the model to faithfully reproduce an identity across novel contexts. To address these issues, we present AnyID, an ultra-fidelity identity-preservation video generation framework that features two core contributions. First, we introduce a scalable omni-referenced architecture that effectively unifies heterogeneous identity inputs (e.g., faces, portraits, and videos) into a cohesive representation. Second, we propose a primary-referenced generation paradigm, which designates one reference as a canonical anchor and uses a novel differential prompt to enable precise, attribute-level controllability. We conduct training on a large-scale, meticulously curated dataset to ensure robustness and high fidelity, and then perform a final fine-tuning stage using reinforcement learning. This process leverages a preference dataset constructed from human evaluations, where annotators performed pairwise comparisons of videos based on two key criteria: identity fidelity and prompt controllability. Extensive evaluations validate that AnyID achieves ultra-high identity fidelity as well as superior attribute-level controllability across different task settings. Project page: https://johnneywang.github.io/AnyID-webpage.

Visual localization, i.e., the problem of estimating the camera pose from which an image was taken, is an important part of applications such as augmented reality and autonomous robots. Many of these applications require a compact memory footprint. Thus, a considerable amount of work has been spent on designing memory-efficient scene representations for visual localization. In this paper, we focus on compressing the 3D structure of the scene by selecting a subset of points from a Structure-from-Motion (SfM) point cloud. In contrast to prior work, which aims to solve (complex) optimization problems, we propose a simple strategy that is almost trivial to implement. Our compression strategy is based on the idea of selecting triplets of points such that the camera pose of each database image (used to build the SfM point cloud) can be accurately estimated from these triplets. Despite its simplicity, our strategy performs similarly to or better than current state-of-the-art structure compression approaches. Combined with standard product quantization approaches to compress feature descriptors, our approach compares favorably with recent learning-based approaches for compact visual localization.

Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed "BBox and Index as Visual Prompt" (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the U-RxnDiagram-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry.

Video temporal grounding (VTG) is a critical task in video understanding and a key capability for extending video large language models (Vid-LLMs) to broader applications. However, existing Vid-LLMs rely on uniform frame sampling to extract video information, resulting in a sparse distribution of key frames and the loss of crucial temporal cues. To address this limitation, we propose Grounded Visual Token Sampling (GroundVTS), a Vid-LLM architecture that focuses on the most informative temporal segments. GroundVTS employs a fine-grained, query-guided mechanism to filter visual tokens before feeding them into the LLM, thereby preserving essential spatio-temporal information and maintaining temporal coherence. Futhermore, we introduce a progressive optimization strategy that enables the LLM to effectively adapt to the non-uniform distribution of visual features, enhancing its ability to model temporal dependencies and achieve precise video localization. We comprehensively evaluate GroundVTS on three standard VTG benchmarks, where it outperforms existing methods, achieving a 7.7-point improvement in mIoU for moment retrieval and 12.0-point improvement in mAP for highlight detection. Code is available at the \href https://github.com/Florence365/GroundVTS GroundVTS code repository .

Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.

Affordance learning is a complex challenge in many applications, where existing approaches primarily focus on the geometric structures, visual knowledge, and affordance labels of objects to determine interactable regions. However, extending this learning capability to a scene is significantly more complicated, as incorporating object- and scene-level semantics is not straightforward; for example, 3D instance identification often struggles with small, interactable, functional parts (i.e., knobs, handles, etc.). In this work, we introduce AffordBridge, a large-scale dataset with 291,637 functional interaction annotations across 685 high-resolution indoor scenes in the form of point clouds. Our affordance annotations are complemented by RGB images that are linked to the same instances within scenes. Building upon our dataset, we propose AffordMatcher, an affordance learning method that establishes coherent semantic correspondences between image-based and point cloud-based instances for keypoint matching, enabling a more precise identification of affordance regions based on cues, so-called visual signifiers. Experimental results on our dataset demonstrate the effectiveness of our approach against other methods. Our code and dataset will be made publicly available.

One-dimensional (1D) visual tokenizers offer notable semantic compactness by discarding local spatial priors, and have become increasingly popular for image reconstruction and generation tasks. However, such global and sequential representations struggle to preserve fine-grained visual content; simply increasing network size or token count offers only superficial mitigation. To address this, we introduce VLTok, a novel 1D hybrid tokenizer that unifies visual and language representations in a shared token space through a self-prompted training paradigm. During training, VLTok simultaneously generates 1D visual and textual tokens from images, aligning the textual tokens with embeddings from a pre-trained language model. This cross-modal alignment infuses implicit linguistic cues into the tokenizer, enhancing fine-grained image encoding. At inference, the self-prompted paradigm eliminates the need for external text, maintaining the simplicity of the image-only framework while benefiting from multi-modal guidance. Extensive experiments on the ImageNet benchmark demonstrate that VLTok achieves state-of-the-art performance in both image reconstruction and image generation. For example, under the same model parameter budget, our method yields relative reduction of 11.1% in rFID and 18.7% in gFID compared to GigaTok.

Diffusion-based video motion customization facilitates the acquisition of human motion representations from a few video samples, while achieving arbitrary subjects transfer through precise textual conditioning. Existing approaches often rely on semantic-level alignment, expecting the model to learn new motion concepts and combine them with other entities (e.g., cats or dogs) to produce visually appealing results. However, video data involve complex spatio-temporal patterns, and focusing solely on semantics cause the model to overlook the visual complexity of motion. Conversely, tuning only the visual representation leads to semantic confusion in representing the intended action. To address these limitations, we propose SynMotion, a new motion-customized video generation model that jointly leverages semantic guidance and visual adaptation. At the semantic level, we introduce the dual-embedding semantic comprehension mechanism which disentangles subject and motion representations, allowing the model to learn customized motion features while preserving its generative capabilities for diverse subjects. At the visual level, we integrate parameter-efficient motion adapters into a pre-trained video generation model to enhance motion fidelity and temporal coherence. Furthermore, we introduce a new embedding-specific training strategy which alternately optimizes subject and motion embeddings, supported by the manually constructed Subject Prior Video (SPV) training dataset. This strategy promotes motion specificity while preserving generalization across diverse subjects. Lastly, we introduce MotionBench, a newly curated benchmark with diverse motion patterns. Experimental results across both T2V and I2V settings demonstrate that SynMotion outperforms existing baselines.

The detection and grounding of multimedia manipulation has emerged as a critical challenge in combating AI-generated disinformation. While existing methods have made progress in recent years, we identify two fundamental limitations in current approaches: (1) Underestimation of MLLM-driven deception risk: prevailing techniques primarily address rule-based text manipulations, yet fail to account for sophisticated misinformation synthesized by multimodal large language models (MLLMs) that can dynamically generate semantically coherent, contextually plausible yet deceptive narratives conditioned on manipulated images; (2) Unrealistic misalignment artifacts: currently focused scenarios rely on artificially misaligned content that lacks semantic coherence, rendering them easily detectable. To address these gaps holistically, we propose a new adversarial pipeline that leverages MLLMs to generate high-risk disinformation. Our approach begins with constructing the MLLM-Driven Synthetic Multimodal (MDSM) dataset, where images are first altered using state-of-the-art editing techniques and then paired with MLLM-generated deceptive texts that maintain semantic consistency with the visual manipulations. Building upon this foundation, we present the Artifact-aware Manipulation Diagnosis via MLLM (AMD) framework featuring two key innovations: Artifact Pre-perception Encoding strategy and Manipulation-Oriented Reasoning, to tame MLLMs for the MDSM problem. Comprehensive experiments validate our framework's superior generalization capabilities as a unified architecture for detecting MLLM-powered multimodal deceptions. In cross-domain testing on the MDSM dataset, AMD achieves the best average performance, with ACC, mAP, and mIoU scores of 88.18, 60.25, and 61.02, respectively.

Current video avatar generation methods excel at identity preservation and motion alignment but lack genuine agency--they cannot autonomously pursue long-term goals through adaptive environmental interaction. We address this by introducing L-IVA (Long-horizon Interactive Visual Avatar), a task and benchmark for evaluating goal-directed planning in stochastic generative environments, and ORCA (Online Reasoning and Cognitive Architecture), the first framework enabling active intelligence in video avatars. ORCA embodies Internal World Model (IWM) capabilities through two key innovations: (1) a closed-loop OTAR cycle (Observe-Think-Act-Reflect) that maintains robust state tracking under generative uncertainty by continuously verifying predicted outcomes against actual generations, and (2) a hierarchical dual-system architecture where System 2 performs strategic reasoning with state prediction while System 1 translates abstract plans into precise, model-specific action captions. By formulating avatar control as a POMDP and implementing continuous belief updating with outcome verification, ORCA enables autonomous multi-step task completion in open-domain scenarios. Extensive experiments demonstrate that ORCA significantly outperforms open-loop and non-reflective baselines in task success rate and behavioral coherence, validating our IWM-inspired design for advancing video avatar intelligence from passive animation to active, goal-oriented behavior.

This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question---an ability that has recently emerged in proprietary systems such as NanoBanana and GPT-Image. To also bring this capability to open-source models, we introduce VQ-VA World, a data-centric framework built around an agentic pipeline for large-scale, targeted data construction. Leveraging web-scale deployment, this pipeline crawls a massive amount of ~1.8M high-quality, interleaved image-text samples for model training. For evaluation, we further release IntelligentBench, a human-curated benchmark that systematically assesses VQ-VA along the aspects of world knowledge, design knowledge, and reasoning. Training with VQ-VA World data yields strong empirical gains: it helps LightFusion attain 53.06 on IntelligentBench, substantially surpassing the best prior open-source baselines (i.e., 7.78 from vanilla LightFusion; 1.94 from UniWorld-V1), and significantly narrowing the gap toward leading proprietary systems (e.g., 81.67 from NanoBanana; 82.64 from GPT-Image). By releasing the full suite of model weights, datasets, and pipelines, our work will greatly stimulate future research on VQ-VA. Our project is publicly available at https://github.com/chenhuigou/VQ-VA-World.

Vision-Language Models (VLMs) typically rely on static initial frames for video reasoning, restricting their ability to incorporate essential dynamic information as the reasoning process evolves. Existing methods that augment Chain-of-Thought (CoT) with additional frame information often exhibit suboptimal CoT quality and lack the crucial ability to synthesize visual information for hypothetical or counterfactual scenarios. We introduce Act-to-See (Act2See), a novel framework that enables active visual perception by empowering VLMs to actively interleave video frames within text CoTs. Act2See is developed via Supervised Fine-Tuning (SFT) on a high-quality dataset of reasoning traces generated by a frontier VLM. These traces integrate active calls to either retrieve existing frames or generate new ones, and are rigorously verified against human-annotated CoTs to ensure quality. This approach cultivates an emergent capability: at inference time, the model actively determines when to search for or synthesize the necessary visual evidence. Act2See establishes new state-of-the-art results on challenging benchmarks, including VideoEspresso and ViTIB, and outperforms comparable or larger models on Video-MME, EgoNormia, and VCR-Bench, demonstrating an advancement in enabling VLMs with active visual perception for video reasoning. Code: https://github.com/martinmamql/act2see.

The performance of multimodal learning systems, particularly in high-stakes domains like automated depression recognition, is fundamentally constrained by the challenge of learning robust visual representations from limited and complex clinical data. To overcome this, we introduce Cross-Modal Guided Visual Synthesis (CMG-VS), a novel training framework that internally enhances the learning process by synthesizing new, task-relevant visual features. At its core, CMG-VS leverages the rich context from audio and text modalities to guide a conditional generative model. This model learns the intricate mapping from speech and language to visual expression, generating a diverse manifold of plausible visual behaviors to enrich the training distribution. Crucially, this synthesis is not a separate pre-processing step. Through a task-guided joint optimization scheme, the generative process is dynamically steered by the downstream multimodal recognizer's performance. This closed-loop feedback mechanism ensures the synthesized visual features are optimized to be maximally discriminative for the recognition task, rather than merely realistic. Comprehensive experiments on the widely-used DAIC-WOZ and E-DAIC benchmark datasets demonstrate that CMG-VS significantly outperforms existing state-of-the-art methods across all standard regression and classification metrics. Ablation studies further validate that our task-guided synthesis is the key driver of this performance gain, proving its effectiveness as a new paradigm for robust multimodal representation learning.

Rapidly-exploring random trees (RRTs) have been widely adopted for robot motion planning due to their robustness and theoretical guarantees. However, existing RRT-based planners require explicit goal configurations specified as numerical joint angles, while many practical applications provide goal specifications through visual observations such as images or demonstration videos where precise goal configurations are unavailable. In this paper, we propose visual-RRT (vRRT), a motion planner that enables visual-goal planning by unifying gradient-based exploitation from differentiable robot rendering with sampling-based exploration from RRTs. We further introduce (1) a frontier-based exploration-exploitation strategy that adaptively prioritizes visually promising search regions, and (2) inertial gradient tree expansion that inherits optimization states across tree branches for momentum-consistent gradient exploitation. Extensive experiments across various robot manipulators including Franka, UR5e, and Fetch demonstrate that vRRT achieves effective visual-goal planning in both simulated and real-world settings, bridging the gap between sampling-based planning and vision-centric robot applications. Our code is available at https://sgvr.kaist.ac.kr/Visual-RRT.

Rearranging disarrayed objects to their intended goal states requires the agent to comprehend the changes that have occurred in the scene and to reason about the process of these changes. To address this, we propose a novel perspective on the visual rearrangement task, drawing inspiration from the diffusion processes in molecular thermodynamics. We model the room shuffle and unshuffle stages as the forward and reverse processes of diffusion. In contrast to conventional methods that rely on scene modeling and differential comparisons, our approach provides insight into the intrinsic evolution process between the goal and initial states of the scene, which allows for a more reasonable rearrangement of objects through fine-grained and progressive denoising steps with high confidence. By analyzing the task objectives, we represent the scene via spatial distributions of objects and model the visual rearrangement process using a diffusion bridge model. Building upon this, we introduce the Diffusion Rearrangement model, which takes point cloud data as input, fits it into Gaussian mixture distributions to represent the states of objects, and predicts the rearrangement target through an iterative denoising transformer. Experimental results on the RoomR dataset demonstrate the effectiveness of our approach.

We present the first scalable framework for training sound source localization (SSL) models using synthetic data from text-to-X models. Although SSL has made notable progress, existing models remain constrained by limited-scale, uncurated real-world datasets that often suffer from semantic misalignment. Furthermore, the introduction of new SSL tasks and benchmarks has increased the need for more generalizable models. To address these challenges, we leverage synthetic data to create synthetic clones of the VGGSound dataset, enabling both fully synthetic and hybrid real-synthetic training. We demonstrate that synthetic data can effectively replace, refine, and scale real training datasets. Extensive experiments across multiple benchmarks show that synthetic data not only matches real data in performance but also enables significant improvements when combined with real samples. Our findings provide the first systematic evidence that synthetic data can serve as a scalable and effective approach for advancing SSL models.

Large-scale pre-trained image-text models exhibit robust multimodal representation, yet applying contrastive language-image pretraining (CLIP) to audio-visual localization remains challenging. Replacing the classification token ([CLS]) with an audio-embedded token ([V_A])struggles to capture semantic cues, and the prompt "a photo of a [V_A]" fails to establish meaningful connections between audio embeddings and context tokens. To address these issues, we propose sound-aware prompt learning (SouPLe), which replaces fixed prompts with learnable context tokens. These tokens incorporate visual features to generate conditional context for a mask decoder, effectively bridging semantic correspondence between audio and visual inputs. Experiments on VGGSound, SoundNet, and AVSBench confirm that SouPLe significantly improves localization and segmentation performance.

Model reprogramming adapts pretrained models to downstream tasks by modifying their input and output spaces. Visual reprogramming, as a prominent instance, has been explored in pioneer works on CLIP, which introduces learnable input transformations as visual prompts to repurpose its visual-language alignment for downstream visual tasks. Existing VR methods focus on single-level alignment between prompted images and text descriptions, overlooking inherent structural information in data that facilitates alignment: semantic granularity from label hierarchies and visual granularity from multi-scale representations. To address this gap, we propose Dual Granularity Alignment (DGA) with two key components for multi-level fusion. For visual granularity, we generate multi-scale images and introduce Uncertainty-calibrated Prediction Fusion (UPF), which fuses predictions based on uncertainty estimation to capture hierarchical spatial information. For semantic granularity, we construct category hierarchies via Prototype-guided Label Hierarchization and develop Hierarchical Knowledge Propagation (HKP), which transfers superclass knowledge for coherent multi-level visual prompts alignment. Our DGA collaboratively integrates both granularities to enhance alignment effectiveness. Experiments across 12 downstream datasets demonstrate DGA's superiority over baselines on both ViT-based and ResNet-based CLIP architectures. Specifically, DGA achieves a 4.5% improvement over the previous state-of-the-art method on ViT-16-based CLIP. By explicitly modeling structural granularities, DGA establishes a new paradigm for visual reprogramming.

We present ARTrack-AC, a new step in the autoregressive tracking paradigm that introduces adaptive capacity inference to achieve both temporal consistency and dynamic efficiency. While existing autoregressive trackers predict object states sequentially with fixed inference capacity, they fail to accommodate the fluctuating temporal difficulty of real videos. ARTrack-AC addresses this limitation by equipping the tracker with the ability to modulate its inference capacity over time. A diffusion-based difficulty estimator anticipates the stability of upcoming segments, guiding a controller to switch between an accurate (high-capacity) and an efficient (low-capacity) mode while maintaining autoregressive consistency. This system-level autoregression extends conventional sequence modeling beyond "what to predict" toward "how to predict," forming a self-regulated tracking process that aligns inference cost with temporal complexity. Despite its simplicity, ARTrack-AC achieves state-of-the-art accuracy-speed trade-off on major benchmarks--66.7% AUC on LaSOT and 47.5% AUC on LaSOText--running 2.9xfaster than its predecessor.