GenAI4ELab/papercli-papers huggingface dataset:
PhysGM: Large Physical Gaussian Model for Feed-Forward 4D Synthesis
Authors: Chunji Lv; Zequn Chen; Donglin Di; Weinan Zhang; Hao Li; Chen Wei; Yinjie Lei; Changsheng Li
Venue: CVPR 2026 (main)

Abstract: Despite advances in physics-based 3D motion synthesis, current methods face key limitations: reliance on pre-reconstructed 3D Gaussian Splatting (3DGS) built from dense multi-view images with time-consuming per-scene optimization; physics integration via either inflexible, hand-specified attributes or unstable, optimization-heavy guidance from video models using Score Distillation Sampling (SDS); and naive concatenation of prebuilt 3DGS with physics modules, which ignores physical information embedded in appearance and yields suboptimal performance. To address these issues, we propose PhysGM, a feed-forward framework that jointly predicts 3D Gaussian representation and physical properties from a single image, enabling immediate simulation and high-fidelity 4D rendering. Unlike slow appearance-agnostic optimization methods, we first pre-train a physics-aware reconstruction model that directly infers both Gaussian and physical parameters. We further refine the model with Direct Preference Optimization (DPO), aligning simulations with the physically plausible reference videos and avoiding the high-cost SDS optimization. To address the absence of a supporting dataset for this task, we propose PhysAssets, a dataset of 50K+ 3D assets annotated with physical properties and corresponding reference videos. Experiments show that PhysGM produces high-fidelity 4D simulations from a single image in one minute, achieving a significant speedup over prior work while delivering realistic renderings.
EmoStyle: Emotion-Driven Image Stylization
Authors: Jingyuan Yang; Zihuan Bai; Hui Huang
Venue: CVPR 2026 (main)

Abstract: Art has long been a profound medium for expressing emotions.While existing image stylization methods effectively transform visual appearance, they often overlook the emotional impact carried by styles.To bridge this gap, we introduce Affective Image Stylization (AIS), a task that applies artistic styles to evoke specific emotions while preserving content.We present EmoStyle, a framework designed to address key challenges in AIS, including the lack of training data and the emotion-style mapping.First, we construct EmoStyleSet, a content-emotion-stylized image triplet dataset derived from ArtEmis to support AIS.We then propose an Emotion-Content Reasoner that adaptively integrates emotional cues with content to learn coherent style queries.Given the discrete nature of artistic styles, we further develop a Style Quantizer that converts continuous style features into emotion-related codebook entries.Extensive qualitative and quantitative evaluations, including user studies, demonstrate that EmoStyle enhances emotional expressiveness while maintaining content consistency.Moreover, the learned emotion-aware style dictionary is adaptable to other generative tasks, highlighting its potential for broader applications.Our work establishes a foundation for emotion-driven image stylization, expanding the creative potential of AI-generated art.
AcTTA: Rethinking Test-Time Adaptation via Dynamic Activation
Authors: Hyeongyu Kim; Geonhui Han; Dosik Hwang
Venue: CVPR 2026 (main)

Abstract: Test-time adaptation (TTA) aims to mitigate performance degradation under distribution shifts by updating model parameters during inference. Existing approaches have primarily framed adaptation around affine modulation, focusing on recalibrating normalization layers. This perspective, while effective, overlooks another influential component in representation dynamics: the activation function. We revisit this overlooked space and propose AcTTA, an activation-aware framework that reinterprets conventional activation functions from a learnable perspective and updates them adaptively at test time. AcTTA reformulates conventional activation functions (e.g., ReLU, GELU) into parameterized forms that shift their response threshold and modulate gradient sensitivity, enabling the network to adjust activation behavior under domain shifts. This functional reparameterization enables continuous adjustment of activation behavior without modifying network weights or requiring source data. Despite its simplicity, AcTTA achieves robust and stable adaptation across diverse corruptions. Across CIFAR10-C, CIFAR100-C, and ImageNet-C, AcTTA consistently surpasses normalization-based TTA methods. Our findings highlight activation adaptation as a compact and effective route toward domain-shift-robust test-time learning, broadening the prevailing affine-centric view of adaptation.
Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement
Authors: Lixuan Chen; Zhongnan Liu; Jesse Hamilton; James M. Balter; Jeong Joon Park; Liyue Shen
Venue: CVPR 2026 (main)

Abstract: Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fast motion estimation from currently acquired measurements. However, prospective reconstruction remains challenging due to ultra-sparse sampling and stringent latency requirements. In this work, we propose PDMR, an Prospective Dynamic 3D MRI Reconstruction framework with latent-space motion tracking. Our core idea is to learn an efficient and generalizable latent manifold of motion fields offline, enabling rapid online adaptation for prospective reconstruction. Specifically, we parameterize the deformation vector fields (DVFs) on a low-dimensional manifold, effectively reducing the search space for fast online adaptation, and employ a tri-plane representation to achieve geometry-aware and memory-efficient encoding of 3D motion. Experiments on both XCAT digital phantoms and in-house abdominal MRI datasets demonstrate that PDMR achieves high-fidelity and temporally consistent reconstruction across multiple prospective scenarios (Immediate and After-2min), outperforming state-of-the-art retrospective and online methods. Our results suggest a promising pathway toward ultra-fast, motion-aware prospective MRI reconstruction in clinical practice.
STARFlow-V: End-to-End Video Generative Modeling with Autoregressive Normalizing Flows
Authors: Jiatao Gu; Ying Shen; Tianrong Chen; Laurent Dinh; Yuyang Wang; Miguel Angel Bautista; David Berthelot; Josh Susskind; Shuangfei Zhai
Venue: CVPR 2026 (main)

Abstract: Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are substantially higher, state-of-the-art systems almost exclusively rely on diffusion-based models. In this work, we revisit this design space by presenting STARFlow-V, a normalizing flow-based video generator with substantial benefits such as end-to-end learning, robust causal prediction and native likelihood estimation. Building upon the recently proposed STARFlow, STARFlow-V operates in the spatiotemporal latent space with a global-local architecture which restricts causal dependencies to a global latent space while preserving rich local within-frame interactions. This eases error accumulation over time, a common pitfall of standard autoregressive diffusion model generation. Additionally, we propose flow-score matching, which equips the model with a light-weight causal denoiser to improve the video generation consistency in an autoregressive fashion. To improve the sampling efficiency, STARFlow-V employs a video-aware Jacobi iteration scheme that recasts inner updates as parallelizable iterations without breaking causality. Thanks to the invertible structure, the same model can natively support text-to-video, image-to-video as well as video-to-video generation tasks. Empirically, STARFlow-V achieves strong visual fidelity and temporal consistency with practical sampling throughput relative to diffusion-based baselines. These results present the first evidence, to our knowledge, that NFs are capable of high-quality autoregressive video generation, establishing them as a promising research direction for building world models.
High-Fidelity Virtual Try-On beyond Paired Data Scarcity via Diffusion-based Cycle-Consistent Learning
Authors: Jia Wu; Yijing Dai; Tingfeng Cao; Meiling Wu; Tao Luo; Jian Dong Zhang; Guangming Lu; Xiaoyi Zeng
Venue: CVPR 2026 (main)

Abstract: Diffusion-based virtual try-on methods rely on vast high-quality garment-person pairs, which are scarce in practice due to the high cost of data collection and preprocessing, limiting their performance in real-world scenarios.To overcome this bottleneck, we propose Cycle-Consistent Virtual Try-On (CCVTON), a diffusion-based approach that enables effective training using massive in-the-wild person images. Specifically, CCVTON introduces a Cycle-Consistent Learning (CCL) strategy that just employs a single unified generative model to disentangle a garment from a person image (try-off branch) and transfer it to the same individual (try-on branch), forming a reconstruction cycle. To this end, we first warm up a Unified Diffusion Transformer (UDiT) on open-source paired data to acquire basic try-on and try-off capabilities. When adapting UDiT to in-the-wild person images, we employ a Multi-Criteria Filtering Operation to select high-quality garments disentangled from person images by the pretrained UDiT. These filtered garments are not used as inputs for CCL, but serve as soft constraints for a perceptual regularization loss, preventing the try-off branch from collapsing to trivial copying. In addition, we propose garment-aware mask generation with a two-stage refinement process to suppress garment leakage while maintaining person consistency. Extensive experiments show that CCVTON achieves state-of-the-art results.
Self-guided Semantic Inspection for Zero-Shot Composed Image Retrieval
Authors: Jingjing Zhang; Lei Zhang; Zheren Fu; Bo Hu; Zhendong Mao
Venue: CVPR 2026 (main)

Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images using a composed query of a reference image and a textual modification, without relying on triplet-based supervision. As the two inputs describe related but semantically unaligned information, the key challenge lies in interpreting their cross-modal discrepancy to infer the user's intended semantic modification. Existing ZS-CIR methods mainly adopt a consistency-driven paradigm, training on semantically aligned image-text pairs with alignment or reconstruction objectives. This paradigm enforces cross-modal agreement but overlooks the semantic discrepancies between modalities that naturally arise during inference. To address this issue, we propose DiffComp (Differentiate-then-Compose), a difference-driven self-supervised framework that actively induces and exploits cross-modal discrepancies during training. It stimulates the model to perceive and reconcile semantic differences across visual and textual modalities, thereby improving consistency between training and inference. The framework consists of three components: Contextual Semantic Super-patches that provide localized and coherent visual representations for downstream perception and composition; Phrase-guided Masking that selectively removes text-aligned visual cues to induce controlled cross-modal discrepancies; and Difference-aware Composition that adaptively integrates visual and textual features according to their degree of semantic difference. Extensive experiments on four ZS-CIR benchmarks show that DiffComp achieves state-of-the-art performance and strong generalization. Our code will be released at https://github.com/Orange1999/DiffComp.
Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification
Authors: Kunlun Xu; Haotong Cheng; Jiangmeng Li; Xu Zou; Jiahuan Zhou
Venue: CVPR 2026 (main)

Abstract: Lifelong person re-identification (LReID) aims to learn from varying domains to obtain a unified person retrieval model. Existing LReID approaches typically focus on learning from scratch or a visual classification-pretrained model, while the Vision-Language Model (VLM) has shown generalizable knowledge in a variety of tasks. Although existing methods can be directly adapted to the VLM, since they only consider global-aware learning, the fine-grained attribute knowledge is underleveraged, leading to limited acquisition and anti-forgetting capacity. To address this problem, we introduce a novel VLM-driven LReID approach named Vision-Language Attribute Disentanglement and Reinforcement (VLADR). Our key idea is to explicitly model the universally shared human attributes to improve inter-domain knowledge transfer, thereby effectively utilizing historical knowledge to reinforce new knowledge learning and alleviate forgetting. Specifically, VLADR includes a Multi-grain Text Attribute Disentanglement mechanism that mines the global and diverse local text attributes of an image. Then, an Inter-domain Cross-modal Attribute Reinforcement scheme is developed, which introduces cross-modal attribute alignment to guide visual attribute extraction and adopts inter-domain attribute alignment to achieve fine-grained knowledge transfer. Experimental results demonstrate that our VLADR outperforms the state-of-the-art methods by 1.9%-2.2% and 2.1%-2.5% on anti-forgetting and generalization capacity. Our source code is available at https://github.com/zhoujiahuan1991/CVPR2026-VLADR
SAGA: Source Attribution of Generative AI Videos
Authors: Rohit Kundu; Vishal Mohanty; Hao Xiong; Shan Jia; Athula Balachandran; Amit K. Roy-Chowdhury
Venue: CVPR 2026 (main)

Abstract: The proliferation of generative AI has led to hyper-realistic synthetic videos, escalating misuse risks and outstripping binary real/fake detectors. We introduce \texttt SAGA  (\underline S ource \underline A ttribution of \underline G enerative \underline A I videos), the first comprehensive framework to address the urgent need for AI-generated video source attribution at a large scale. Unlike traditional detection, \texttt SAGA  identifies the specific generative model used. It uniquely provides multi-granular attribution across five levels: authenticity, generation task (e.g., T2V/I2V), model version, development team, and the precise generator, offering far richer forensic insights. Our novel video transformer architecture, leveraging features from a robust vision foundation model, effectively captures spatio-temporal artifacts. Critically, we introduce a data-efficient pretrain-and-attribute strategy, enabling \texttt SAGA  to achieve state-of-the-art attribution using only 0.5% of source-labeled data per class, matching fully supervised performance. Furthermore, we propose Temporal Attention Signatures (\texttt T-Sig ), a novel interpretability method that visualizes learned temporal differences, offering the first explanation for why different video generators are distinguishable. Extensive experiments on public datasets, including cross-domain scenarios, demonstrate that \texttt SAGA  sets a new benchmark for synthetic video provenance, providing crucial, interpretable insights for forensic and regulatory applications.
Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging
Authors: Zhilin Zhu; Yabin Wang; Zhiheng Ma; Yaguang Song; Yaowei Wang; Xiaopeng Hong
Venue: CVPR 2026 (main)

Abstract: Continual Test-Time Adaptation (CTTA) aims to empower perception systems to handle dynamic distribution shifts encountered after deployment. Existing methods predominantly follow a backward-alignment paradigm, which rigidly aligns incoming data with supervisory surrogates derived from the source domain. Consequently, they struggle with unreliable supervision and evolving distribution shifts. To overcome these limitations, we introduce a novel forward-facilitation paradigm through a method termed Dynamic Style Bridging. Prior to deployment, we construct a compact knowledge base of generated class exemplars. During test time, to mitigate inherent generative bias and adapt these proxies to incoming data, we propose a multi-level bridging mechanism. This mechanism dynamically injects the proxies with incoming data styles at the input, statistical, and representation levels, while preserving the original semantics of the proxies. These high-fidelity proxies are then used to provide reliable, on-demand supervisory signals, enabling stable adaptation under continual shifts. Extensive experiments across standard CTTA benchmarks demonstrate that our method achieves consistent and substantial improvements over recent state-of-the-art approaches. Code is available at https://github.com/z1358/DAS.
Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting
Authors: Hyeonseo Jang; Hyuk Kwon; Kibok Lee
Venue: CVPR 2026 (main)

Abstract: We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge using parameter-efficient methods, such as prefix-tuning or adapters, which facilitate model adaptation to downstream tasks by incorporating task-specific information into input tokens through additive vectors. However, previous approaches often normalize the weights of these vectors, disregarding the fact that different input tokens require different degrees of adjustment. To overcome this issue, we propose Dynamic Prefix Weighting (DPW), a framework that dynamically assigns weights to prefixes, complemented by adapters. DPW consists of 1) a gating module that adjusts the weights of each prefix based on the importance of the corresponding input token, and 2) a weighting mechanism that derives adapter output weights as a residual of prefix-tuning weights, ensuring that adapters are utilized only when necessary. Experimental results demonstrate that our method achieves state-of-the-art performance in domain-class incremental learning scenarios for VLMs. The code is available at: https://github.com/YonseiML/dpw.
Temporal Inversion for Learning Interval Change in Chest X-Rays
Authors: Hanbin Ko; Kyeongmin Jeon; Doowoong Choi; Chang Min Park
Venue: CVPR 2026 (main)

Abstract: Recent advances in vision-language pretraining have enabled strong medical foundation models, yet most analyze radiographs in isolation, overlooking the key clinical task of comparing prior and current images to assess interval change. For chest radiographs (CXRs), capturing interval change is essential, as radiologists must evaluate not only the static appearance of findings but also how they evolve over time. We introduce TILA (Temporal Inversion-aware Learning and Alignment), a simple yet effective framework that uses temporal inversion, reversing image pairs, as a supervisory signal to enhance the sensitivity of existing temporal vision--language models to directional change. TILA integrates inversion-aware objectives across pretraining, fine-tuning, and inference, complementing conventional appearance modeling with explicit learning of temporal order. We also propose a unified evaluation protocol to assess order sensitivity and consistency under temporal inversion, and introduce MS-CXR-T_retrieval, a retrieval evaluation set constructed through a general protocol that can be applied to any temporal CXR dataset. Experiments on public datasets and real-world hospital cohorts demonstrate that TILA consistently improves progression classification and temporal embedding alignment when applied to multiple existing architectures.
PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction
Authors: Xiang Zhang; Sohyun Yoo; Hongrui Wu; Chuan Li; Jianwen Xie; Zhuowen Tu
Venue: CVPR 2026 (main)

Abstract: We introduce PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed distance fields and post-hoc layout optimization, PixARMesh jointly predicts object layout and geometry within a unified model, producing coherent and artist-ready meshes in a single forward pass. Building on recent advances in mesh generative models, we augment a point-cloud encoder with pixel-aligned image features and global scene context via cross-attention, enabling accurate spatial reasoning from a single image. Scenes are generated autoregressively from a unified token stream containing context, pose, and mesh, yielding compact meshes with high-fidelity geometry. Experiments on synthetic and real-world datasets show that PixARMesh achieves state-of-the-art reconstruction quality while producing lightweight, high-quality meshes ready for downstream applications.
PS-SR: Pseudo-Single-Step Video Super-Resolution via Speculative Diffusion
Authors: Aiqiu Wu; Zhaofan Qiu; Ting Yao; Tao Mei
Venue: CVPR 2026 (main)

Abstract: Video Super-Resolution (VSR) fundamentally struggles with a critical trade-off: single-step models offer unmatched efficiency but often lack the high-frequency detail, creativity, and visual quality of their multi-step diffusion counterparts, which are computationally prohibitive for practical use. In this paper, we propose PS-SR, a novel "pseudo" single-step VSR framework that transcends this trade-off through a computationally asymmetric sampling pipeline. The key to PS-SR lies in its speculative diffusion mechanism: a powerful base model performs only a single, comprehensive sampling step, establishing the global structure and content fidelity, after which a lightweight draft model, directly augmented by the base model's features, speculatively performs subsequent refinements. Crucially, we further enforce a frequency-domain update rule that constrains these refinements to exclusively inject high-frequency details, preserving the foundational low-frequency content and preventing semantic drift across sampling steps. By doing so, PS-SR creates the "illusion" of a single-step model--delivering the similar inference speeds and input-output content consistency--while achieving the visual richness and creativity typically reserved for costly multi-step generative models. We demonstrate that our "pseudo-single-step" paradigm achieves state-of-the-art quality with a comparable speed to single-step models, paving the way for real-time, high-fidelity video enhancement. Please refer to our project page for more results: https://waq2001.github.io/PS-SR-page/.
ARES: Unifying Asymmetric RGB-Event Stereo for Probabilistic Scene Flow Estimation
Authors: Jie Long Lee; Gim Hee Lee
Venue: CVPR 2026 (main)

Abstract: Estimating dense three dimensional motion in dynamic high speed scenes remains challenging due to motion blur, illumination variation, and the limited temporal resolution of conventional cameras. We introduce ARES, a unified framework for Asymmetric RGB-Event Stereo that addresses these issues through a hybrid setup where an event camera captures fine grained temporal dynamics and an RGB camera provides rich spatial structure. To integrate these heterogeneous modalities, we propose Multimodal Contextual Attention, a transformer based fusion mechanism that attends to spatial and temporal contexts under cross view constraints and forms a unified correspondence space for disparity and optical flow estimation. Building on this shared representation, we introduce Temporal Disparity Posterior Fusion, a probabilistic framework that models the evolution of disparity posteriors to infer disparity change and recover metrically coherent scene flow. Trained with sparse supervision and dense self consistency cues, our ARES achieves geometrically consistent and temporally stable three dimensional motion estimation across diverse driving scenarios. Experiments show that ARES attains state of the art performance in scene flow estimation among RGB-event stereo methods, establishing a principled path toward unified asymmetric multimodal stereo sensing. Code available at the project website.