GenAI4ELab/papercli-papers huggingface dataset:
TempoControl: Temporal Attention Guidance for Text-to-Video Models
Authors: Shira Schiber; Ofir Lindenbaum; Idan Schwartz
Venue: CVPR 2026 (main)

Abstract: Recent advances in generative video models have enabled the creation of high-quality videos based on natural language prompts. However, these models frequently lack fine-grained temporal control, meaning they do not allow users to specify when particular visual elements should appear within a generated sequence. In this work, we introduce TempoControl, a method that allows for temporal alignment of visual concepts during inference, without requiring retraining or additional supervision. TempoControl utilizes cross-attention maps, a key component of text-to-video diffusion models, to guide the timing of concepts through a novel optimization approach. Our method steers attention using three complementary principles: aligning its temporal pattern with a control signal (correlation), adjusting its strength where visibility is required (magnitude), and preserving semantic consistency (entropy). TempoControl provides precise temporal control while maintaining high video quality and diversity. We demonstrate its effectiveness across various applications, including temporal reordering of single and multiple objects, action timing, and audio-aligned video generation.
PanoVGGT: Feed-Forward 3D Reconstruction from Panoramic Imagery
Authors: Yijing Guo; Mengjun Chao; Luo Wang; Tianyang Zhao; Haizhao Dai; Yingliang Zhang; Jingyi Yu; Yujiao Shi
Venue: CVPR 2026 (main)

Abstract: Panoramic imagery offers a full 360^\circ field of view and is increasingly common in consumer devices. However, it introduces non-pinhole distortions that challenge joint pose estimation and 3D reconstruction. Existing feed-forward models, built for perspective cameras, generalize poorly to this setting. We propose PanoVGGT, a permutation-equivariant Transformer framework that jointly predicts camera poses, depth maps, and 3D point clouds from one or multiple panoramas in a single forward pass. The model incorporates spherical-aware positional embeddings and a panorama-specific three-axis SO(3) rotation augmentation, enabling effective geometric reasoning in the spherical domain. To resolve inherent global-frame ambiguity, we further introduce a stochastic anchoring strategy during training. In addition, we contribute PanoCity, a large-scale outdoor panoramic dataset with dense depth and 6-DoF pose annotations. Extensive experiments on PanoCity and standard benchmarks demonstrate that PanoVGGT achieves competitive accuracy, strong robustness, and improved cross-domain generalization. Code and dataset will be released.
MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On
Authors: Xiaoyu Han; Chenyang Wang; Jing Wang; Shunyuan Zheng; Quanling Meng; Shengping Zhang
Venue: CVPR 2026 (main)

Abstract: Virtual try-on aims to fit an in-shop clothing image onto a specific human body. An optimal virtual try-on method should provide diverse and flexible dressing options, accurately reflecting the varied wearing styles encountered in real-life scenarios, tailored to individual preferences and fashion aspirations. However, current methods predominantly perform a direct replacement of the original clothing with the target clothing, following the same dressing pattern. This limited control over clothing adaptation may result in fixed and monotonous try-on outputs. To delve into More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On, we propose a novel virtual try-on method, termed MOFA-VTON, which allows adjustment for clothing adaptations in try-on results through simple sketches by users. Specifically, we first design a mask construction strategy that transforms user-drawn curve sketches into a dual-region mask, replacing the traditional clothing-agnostic mask and providing fine-grained layout guidance for the subsequent generation process. Further, we propose layout adjustment blocks that utilize the cross-attention mechanism to independently learn layout correspondences for upper and lower regions of the human body, refining the spatial arrangement of the two regions. With these implementations, our method enables flexible and fine-grained adaptations of target clothing, overcoming the constraints of a fixed layout. Extensive experiments on VITON-HD and DressCode datasets demonstrate that our proposed MOFA-VTON outperforms previous state-of-the-art methods and provides more fashion possibilities for virtual try-on.
NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction
Authors: Fei Liu; Shichao Xie; Minghua Luo; Zedong Chu; Junjun Hu; Xiaolong Wu; Mu Xu
Venue: CVPR 2026 (main)

Abstract: Embodied navigation for long-horizon tasks, guided by complex natural language instructions, remains a formidable challenge in artificial intelligence. Existing agents often struggle with robust long-term planning about unseen environments, leading to high failure rates. To address these limitations, we introduce NavForesee, a novel Vision-Language Model (VLM) that unifies high-level language planning and predictive world model imagination within a single, unified framework.Our approach empowers a single VLM to concurrently perform planning and predictive foresight. Conditioned on the full instruction and historical observations, the model is trained to understand the navigation instructions by decomposing the task, tracking its progress, and formulating the subsequent sub-goal. Simultaneously, it functions as a generative world model, providing crucial foresight by predicting short-term environmental dynamics and long-term navigation milestones. The VLM's structured plan guides its targeted prediction, while the imagined future provides rich context to inform the navigation actions, creating a powerful internal feedback loop of perception-planning/prediction-action. We demonstrate through extensive experiments on the R2R-CE and RxR-CE benchmark that NavForesee achieves highly competitive performance in complex scenarios. Our work highlights the immense potential of fusing explicit language planning with implicit spatiotemporal prediction, paving the way for more intelligent and capable embodied agents.
RealAppiance: Let High-fidelity Appliance Assets Controllable and Workable as Aligned Real Manauls
Authors: Yuzheng Gao; Yuxing Long; Lei Kang; Yuchong Guo; Ziyan Yu; Shangqing Mao; Jiyao Zhang; Ruihai Wu; Dongjiang Li; Hui Shen; Hao Dong
Venue: CVPR 2026 (main)

Abstract: Existing appliance assets suffer from poor rendering, incomplete mechanisms, and misalignment with manuals, leading to simulation-reality gaps that hinder appliance manipulation development. In this work, we introduce the RealAppliance dataset, comprising 100 high-fidelity appliances with complete physical, electronic mechanisms, and program logic aligned with their manuals. Based on these assets, we propose the RealAppliance-Bench benchmark, which evaluates multimodal large language models and embodied manipulation planning models across key tasks in appliance manipulation planning: manual page retrieval, appliance part grounding, open-loop manipulation planning, and closed-loop planning adjustment. Our analysis of model performances on RealAppliance-Bench provides insights for advancing appliance manipulation research
SJD-PAC: Accelerating Speculative Jacobi Decoding via Proactive Drafting and Adaptive Continuation
Authors: Jialiang Kang; Han Shu; Wenshuo Li; Yingjie Zhai; Xinghao Chen
Venue: CVPR 2026 (main)

Abstract: Speculative Jacobi Decoding (SJD) offers a draft-model-free approach to accelerate autoregressive text-to-image synthesis. However, the high-entropy nature of visual generation yields low draft-token acceptance rates in complex regions, creating a bottleneck that severely limits overall throughput. To overcome this, we introduce SJD-PAC, an enhanced SJD framework. First, SJD-PAC employs a proactive drafting strategy to improve local acceptance rates in these challenging high-entropy regions. Second, we introduce an adaptive continuation mechanism that sustains sequence validation after an initial rejection, bypassing the need for full resampling. Working in tandem, these optimizations significantly increase the average acceptance length per step, boosting inference speed while strictly preserving the target distribution. Experiments on standard text-to-image benchmarks demonstrate that SJD-PAC achieves a 3.8x speedup with lossless image quality.
NitroGen: An Open Foundation Model for Generalist Gaming Agents
Authors: Loïc Magne; Anas Awadalla; Guanzhi Wang; Yinzhen Xu; Joshua Belofsky; Fengyuan Hu; Joohwan Kim; Ludwig Schmidt; Georgia Gkioxari; Jan Kautz; Yisong Yue; Yejin Choi; Yuke Zhu; Linxi Fan
Venue: CVPR 2026 (main)

Abstract: We introduce NitroGen, a vision-action foundation model for generalist gaming agents that is trained on 40,000 hours of gameplay videos across more than 1,000 games. We scale embodied agents through three key ingredients: 1) an internet-scale video-action dataset constructed by automatically extracting player actions from publicly available gameplay videos, 2) a multi-game benchmark environment that can measure cross-game generalization, and 3) a unified vision-action model trained with large-scale behavior cloning. NitroGen exhibits competence across diverse domains, including combat encounters in 3D action games, high-precision control in 2D platformers, and exploration in procedurally generated worlds. When fine-tuned, pre-training transfers effectively to unseen games, achieving up to 52% relative improvement in task success rates over models trained from scratch. We release the dataset, evaluation suite, and model weights to advance research on generalist embodied agents.
Few-Shot Hybrid Incremental Learning:Continually Learning under Data Scarcity and Task Uncertainty
Authors: Yan Li; Yuzhu Shi; Kan Zhou; Shu Zhang; Diqi He; Dingwen Zhang; Junwei Han
Venue: CVPR 2026 (main)

Abstract: The increasing complexity of real-world deployment requires intelligent agents to effectively adapt to non-stationary data streams with stochastic increments under data scarcity. We formally define this challenge as the Few-Shot Hybrid Incremental Learning (FSHIL) paradigm, which reveals a critical stability-plasticity dilemma. Existing strategies struggle to address this dilemma: representation freezing in few-shot incremental learning can mitigate overfitting under data scarcity but leads to insufficient representation plasticity, while architecture expansion in hybrid incremental learning provides plasticity for adaptation but results in overfitting under few-shot conditions. To address this, we propose the Conditional Meta-Expanding Mixture-of-Experts (CME-MoE), which balances feature-level stability-plasticity trade-off through conditional expert reuse and meta-expansion mechanism. Furthermore, recognizing the multi-domain manifestation in the latent space, we introduce the Self-Expanding Prototype Classifier (SEPC), which on-demand expands classification to model complex domain-shifted decision boundaries. The proposed method outperforms existing state-of-the-art methods in three few-shot incremental learning settings across five mainstream datasets, effectively addressing data scarcity and task uncertainty, and providing a robust solution for real-world continual learning.
VEMamba: Efficient Isotropic Reconstruction of Volume Electron Microscopy with Axial-Lateral Consistent Mamba
Authors: Longmi Gao; Pan Gao
Venue: CVPR 2026 (main)

Abstract: Volume Electron Microscopy (VEM) is crucial for 3D tissue imaging but often produces anisotropic data with poor axial resolution, hindering visualization and downstream analysis. Existing methods for isotropic reconstruction often suffer from neglecting abundant axial information and employing simple downsampling to simulate anisotropic data. To address these limitations, we propose VEMamba, an efficient framework for isotropic reconstruction. The core of VEMamba is a novel 3D Dependency Reordering paradigm, implemented via two key components: an Axial-Lateral Chunking Selective Scan Module (ALCSSM), which intelligently re-maps complex 3D spatial dependencies (both axial and lateral) into optimized 1D sequences for efficient Mamba-based modeling, explicitly enforcing axial-lateral consistency; and a Dynamic Weights Aggregation Module (DWAM) to adaptively aggregate these reordered sequence outputs for enhanced representational power. Furthermore, we introduce a realistic degradation simulation and then leverage Momentum Contrast (MoCo) to integrate this degradation-aware knowledge into the network for superior reconstruction. Extensive experiments on both simulated and real-world anisotropic VEM datasets demonstrate that VEMamba achieves highly competitive performance across various metrics while maintaining a lower computational footprint.
DepthFocus: Controllable Depth Estimation for See-Through Scenes
Authors: Junhong Min; Jimin Kim; Minwook Kim; Cheol-Hui Min; Youngpil Jeon; Minyong Choi
Venue: CVPR 2026 (main)

Abstract: Depth in the real world is rarely singular. Transmissive materials create layered ambiguities that confound conventional perception systems. Existing models remain passive; conventional approaches typically estimate static depth maps anchored to the nearest surface, and even recent multi-head extensions suffer from a representational bottleneck due to fixed feature representations. This stands in contrast to human vision, which actively shifts focus to perceive a desired depth. We introduce DepthFocus, a steerable Vision Transformer that redefines stereo depth estimation as condition-aware control. Instead of extracting fixed features, our model dynamically modulates its computation based on a physical reference depth, integrating dual conditional mechanisms to selectively perceive geometry aligned with the desired focus. Leveraging a newly curated large-scale synthetic dataset, DepthFocus achieves state-of-the-art results across all evaluated benchmarks, including both standard single-layer and complex multi-layered scenarios. While maintaining high precision in opaque regions, our approach effectively resolves depth ambiguities in transparent and reflective scenes by selectively reconstructing geometry at a target distance. This capability enables robust, intent-driven perception that significantly outperforms existing multi-layer methods, marking a substantial step toward active 3D perception. \noindent Project page: \href https://junhong-3dv.github.io/depthfocus-project/  this https URL .
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models
Authors: Yucheng Xie; Fu Feng; Ruixiao Shi; Jianlu Shen; Jing Wang; Yong Rui; Xin Geng
Venue: CVPR 2026 (main)

Abstract: The training of diffusion models is computationally intensive, making effective pre-training essential. However, real-world deployments often demand models of variable sizes due to diverse memory and computational constraints, posing challenges when corresponding pre-trained versions are unavailable.To address this, we propose FINE, a novel pre-training method whose resulting model can flexibly factorize its knowledge into fundamental components, termed learngenes, enabling direct initialization of models of various sizes and eliminating the need for repeated pre-training.Rather than optimizing a conventional full-parameter model, FINE represents each layer's weights as the product of U_ \star , \Sigma_ \star ^ (l) , and V_ \star ^\top, where U_ \star  and V_ \star  serve as size-agnostic learngenes shared across layers, while \Sigma_ \star ^ (l)  remains layer-specific.By jointly training these components, FINE forms a decomposable and transferable knowledge structure that allows efficient initialization through flexible recombination of learngenes, requiring only light retraining of \Sigma_ \star ^ (l)  on limited data.Extensive experiments demonstrate the efficiency of FINE, achieving state-of-the-art performance in initializing variable-sized models across diverse resource-constrained deployments. Furthermore, models initialized by FINE effectively adapt to diverse tasks, showcasing the task-agnostic versatility of learngenes.
OctoNav: Towards Generalist Embodied Navigation
Authors: Chen Gao; Liankai Jin; Xingyu Peng; Jiazhao Zhang; Yue Deng; Annan Li; He Wang; Si Liu
Venue: CVPR 2026 (main)

Abstract: Embodied navigation stands as a foundation pillar within the pursuit of embodied intelligence. However, previous navigation research is divided into different tasks/capabilities, e.g., ObjNav, ImgNav and VLN, where they differ in task settings/objectives and modalities, making datasets and methods designed individually. In this work, we take steps toward generalist navigation, which can follow free-form instructions that include arbitrary compounds of modality and capability. To achieve this, we propose a large-scale benchmark and corresponding method, termed OctoNav-Bench and OctoNav-R1. Specifically, OctoNav-Bench is constructed via a designed automatic annotation pipeline. We thoroughly craft instruction-trajectory pairs, where instructions are diverse in free-form with arbitrary modality and capability. Also, we construct a Think-Before-Action (TBA-CoT) dataset within OctoNav-Bench to provide the thinking process behind actions. For OctoNav-R1, we build it upon MLLMs and adapt it to a VLA-type model, which can produce low-level actions solely based on 2D visual observations. Moreover, we design a Hybrid Training Paradigm (HTP) that consists of three stages, i.e., Action-/TBA-SFT, Nav-GRPO, and Online RL stages. Each stage contains designed learning policies and rewards. Specifically, inspired by the OpenAI-o1 and DeepSeek-R1, which show impressive reasoning ability via thinking-before-answer, we design TBA-SFT and Nav-GRPO to achieve thinking-before-action for embodied navigation, improving model's reasoning ability toward generalists. TBA-SFT utilizes the TBA-CoT dataset to fine-tune the model, and then we leverage Nav-GRPO to improve its thinking ability. Finally, OctoNav-R1 shows superior performance compared with the previous methods.
HumanNOVA: Photorealistic, Universal and Rapid 3D Human Avatar Modeling from a Single Image
Authors: Hezhen Hu; Wangbo Zhao; Lanqing Guo; Hanwen Jiang; Jonathan C. Liu; Zhiwen Fan; Kai Wang; Zhangyang Wang; Georgios Pavlakos
Venue: CVPR 2026 (main)

Abstract: In this paper, we present HumanNOVA, a photorealistic, universal, and rapid model for generating 3D human avatars from a single RGB image. Achieving both photorealism and generalization is challenging due to the scarcity of diverse, high-quality 3D human data. To address this, we build a scalable data generation pipeline that follows two strategies. The first one is to leverage existing rigged assets and animate them with extensive poses from daily life. The second strategy is to utilize existing multi-camera captures of humans and employ fitting to generate more diverse views for training. These two strategies enable us to scale up to 100k assets, significantly enhancing both the quantity and the diversity of data for robust model training. In terms of the architecture, HumanNOVA adopts a feed-forward, token-conditioned avatar modeling framework that allows fast inference in less than one second and requires no test-time optimization. Given an input image and an estimated simplified human mesh (SMPL) without detailed geometry or appearance, the model first encodes both inputs into compact token representations. These tokens then act as conditioning signals and are fused through cross-attention to construct a triplane-based 3D avatar representation. Extensive experiments on multiple benchmarks demonstrate the superiority of our approach, both quantitatively and qualitatively, as well as its robustness under diverse input image conditions. Project page at https://HumanNOVA.github.io.
Semantic Alignment for Pose-Invariant Identity Preserving Diffusion
Authors: Jiwon Kim; SeonHwa Kim; Soobin Park; Eunju Cha; Kyong Hwan Jin
Venue: CVPR 2026 (main)

Abstract: Recent T2I diffusion models have evolved to control multiple conditions, including structure, appearance, and text prompt. Despite this progress, training-based methods demand heavy computation, whereas training-free methods often 're-imagine' the subject to satisfy given structure, thereby compromising identity preservation and attenuating fine textures.We propose SeAl (Semantic Alignment for Pose-Invariant Identity Preserving Diffusion), a novel training-free framework that addresses the 're-imagining' problem from the perspective of 'infusion'. SeAl integrates structure, appearance, and text prompt with three modules: AnchorAlign pre-aligns spatial discrepancies, Reference-guided Appearance Infusion injects identity via semantic matching, and Delta-Bridge leverages the guidance delta to mediate text-appearance conflicts. We demonstrate that our method faithfully reflects all three control factors and dramatically reduces the identity leakage endemic to prior methods. Notably, SeAl excels on challenging datasets where identity preservation typically fails (e.g., distinctive animal features or complex human attire), establishing a novel paradigm for training-free identity preservation in diffusion models.
ULF-Loc: Unbiased Landmark Feature for Robust Visual Localization with 3D Gaussian Splatting
Authors: Yingdong Gu; Shaocheng Yan; Zhenjun Zhao; Yuan Kou; Jianxin Luo; Pengcheng Shi; Jiayuan Li
Venue: CVPR 2026 (main)

Abstract: Visual localization is a core technology for augmented reality and autonomous navigation. Recent methods combine the efficient rendering of 3D Gaussian Splatting (3DGS) with feature-based localization. These methods rely on direct matching between 2D query features and the 3D Gaussian feature field, but this often results in mismatches due to an inherent bias in the learned Gaussian feature. We theoretically analyze the feature learning process in 3DGS, revealing that the widely adopted alpha-blending optimization inherently introduces bias into 3D point features. This bias stems from the entanglement between individual Gaussians and their neighboring Gaussians, making the learned features unsuitable for precise matching tasks. Motivated by these findings, we propose ULF-Loc, an unbiased landmark feature framework that replaces biased feature optimization with geometry-weighted feature fusion. We further introduce keypoint-consensus landmark sampling to select reliable Gaussians and local geometric consistency verification to reject mismatches caused by rendering artifacts. On the Cambridge Landmarks dataset, ULF-Loc reduces the mean median translation error by 17% compared to the state-of-the-art, while achieving superior efficiency with only 1/10 the training time and 1/6 the GPU memory of STDLoc. Our code is accessible at https://github.com/Cyril-gyd/ULF-Loc.