GenAI4ELab/papercli-papers huggingface dataset:
CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring
Authors: Mingchen Zhong; Xin Lu; Dong Li; Senyan Xu; Ruixuan Jiang; Xueyang Fu; Baocai Yin
Venue: AAAI 2026 (main)

Abstract: Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event cameras offer potential solutions with superior low-light sensitivity and high temporal resolution, existing fusion methods typically employ staged strategies, limiting their effectiveness against combined low-light and motion blur degradations. To overcome this, we propose CompEvent, a complex neural network framework enabling holistic full-process fusion of event data and RGB frames for enhanced joint restoration. CompEvent features two core components: 1) Complex Temporal Alignment GRU, which utilizes complex-valued convolutions and processes video and event streams iteratively via GRU to achieve temporal alignment and continuous fusion; and 2) Complex Space-Frequency Learning module, which performs unified complex-valued signal processing in both spatial and frequency domains, facilitating deep fusion through spatial structures and system-level characteristics. By leveraging the holistic representation capability of complex-valued neural networks, CompEvent achieves full-process spatiotemporal fusion, maximizes complementary learning between modalities, and significantly strengthens low-light video deblurring capability. Extensive experiments demonstrate that CompEvent outperforms SOTA methods in addressing this challenging task.
Empowering Sparse-Input Neural Radiance Fields with Dual-Level Semantic Guidance from Dense Novel Views
Authors: Yingji Zhong; Kaichen Zhou; Zhihao Li; Lanqing Hong; Zhenguo Li; Dan Xu
Venue: AAAI 2026 (main)

Abstract: Neural Radiance Fields (NeRF) have shown remarkable capabilities
for photorealistic novel view synthesis. One major deficiency of
NeRF is that dense inputs are typically required, and the rendering
quality will drop drastically given sparse inputs. In this paper, we
highlight the effectiveness of rendered semantics from dense novel
views, and show that rendered semantics can be treated as a more
robust form of augmented data than rendered RGB. Our method
enhances NeRF’s performance by incorporating guidance derived
from the rendered semantics. The rendered semantic guidance encompasses
two levels: the supervision level and the feature level.
The supervision-level guidance incorporates a bi-directional verification
module that decides the validity of each rendered semantic
label, while the feature-level guidance integrates a learnable codebook
that encodes semantic-aware information, which is queried
by each point via the attention mechanism to obtain semanticrelevant
predictions. The overall semantic guidance is embedded
into a self-improved pipeline.We also introduce a more challenging
sparse-input indoor benchmark, where the number of inputs is
limited to as few as 6. Experiments demonstrate the effectiveness
of our method and it exhibits superior performance compared to
existing approaches.
MoReMouse: Monocular Reconstruction of Laboratory Mouse
Authors: Yuan Zhong; Jingxiang Sun; Zhongbin Zhang; Liang An; Yebin Liu
Venue: AAAI 2026 (main)

Abstract: Laboratory mice, particularly the C57BL/6 strain, are essential animal models in biomedical research. However, accurate 3D surface motion reconstruction of mice remains a significant challenge due to their complex non-rigid deformations, textureless fur-covered surfaces, and the lack of realistic 3D mesh models. Moreover, existing visual datasets for mice reconstruction only contain sparse viewpoints without 3D geometries. To fill the gap, we introduce MoReMouse, the first monocular dense 3D reconstruction network specifically designed for C57BL/6 mice. To achieve high-fidelity 3D reconstructions, we present three key innovations. First, we create the first high-fidelity, dense-view synthetic dataset for C57BL/6 mice by rendering a realistic, anatomically accurate Gaussian mouse avatar. Second, MoReMouse leverages a transformer-based feedforward architecture combined with triplane representation, enabling high-quality 3D surface generation from a single image, optimized for the intricacies of small animal morphology. Third, we propose geodesic-based continuous correspondence embeddings on the mouse surface, which serve as strong semantic priors, improving surface consistency and reconstruction stability, especially in highly dynamic regions like limbs and tail.
Through extensive quantitative and qualitative evaluations, we demonstrate that MoReMouse significantly outperforms existing open-source methods in both accuracy and robustness.
TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction
Authors: Yuxiang Zhong; Jun Wei; Chaoqi Chen; Senyou An; Hui Huang
Venue: AAAI 2026 (main)

Abstract: 3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation with superior efficiency and quality. While recent adaptations for computed tomography (CT) show promise, they struggle with severe artifacts under highly sparse-view projections and dynamic motions. To address these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored for both static and dynamic CT reconstruction. A multi-resolution hash encoder is employed to capture local spatial priors, regularizing primitive parameters under ultra-sparse settings. We further extend the framework to dynamic reconstruction by introducing time-conditioned representations and a spatiotemporal attention block to adaptively aggregate features, thereby resolving spatiotemporal ambiguities and enforcing temporal coherence. In addition, a motion-flow network models fine-grained respiratory motion to track local anatomical deformations. Extensive experiments on synthetic and real-world datasets demonstrate that TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.
Splat-SAP: Feed-Forward Gaussian Splatting for Human-Centered Scene with Scale-Aware Point Map Reconstruction
Authors: Boyao Zhou; Shunyuan Zheng; Zhanfeng Liao; Zihan Ma; Hanzhang Tu; Boning Liu; Yebin Liu
Venue: AAAI 2026 (main)

Abstract: We present Splat-SAP, a feed-forward approach to render novel views of human-centered scenes from binocular cameras with large sparsity. Gaussian Splatting has shown its promising potential in rendering tasks, but it typically necessitates per-scene optimization with dense input views. Although some recent approaches achieve feed-forward Gaussian Splatting rendering through geometry priors obtained by multi-view stereo, such approaches still require largely overlapped input views to establish the geometry prior. To bridge this gap, we leverage pixel-wise point map reconstruction to represent geometry which is robust to large sparsity for its independent view modeling. In general, we propose a two-stage learning strategy. In stage 1, we transform the point map into real space via an iterative affinity learning process, which facilitates camera control in the following. In stage 2, we project point maps of two input views onto the target view plane and refine such geometry via stereo matching. Furthermore, we anchor Gaussian primitives on this refined plane in order to render high-quality images. As a metric representation, the scale-aware point map in stage 1 is trained in a self-supervised manner without 3D supervision and stage 2 is supervised with photo-metric loss. We collect multi-view human-centered data and demonstrate that our method improves both the stability of point map reconstruction and the visual quality of free-viewpoint rendering.
M3ashy: Multi-Modal Material Synthesis via Hyperdiffusion
Authors: Chenliang Zhou; Zheyuan Hu; Alejandro Sztrajman; Yancheng Cai; Yaru Liu; Cengiz Oztireli
Venue: AAAI 2026 (main)

Abstract: High-quality material synthesis is essential for replicating complex surface properties to create realistic scenes. Despite advances in the generation of material appearance based on analytic models, the synthesis of real-world measured BRDFs remains largely unexplored. To address this challenge, we propose M^3ashy, a novel multi-modal material synthesis framework based on hyperdiffusion. M^3ashy enables high-quality reconstruction of complex real-world materials by leveraging neural fields as a compact continuous representation of BRDFs. Furthermore, our multi-modal conditional hyperdiffusion model allows for flexible material synthesis conditioned on material type, natural language descriptions, or reference images, providing greater user control over material generation. To support future research, we contribute two new material datasets and introduce two BRDF distributional metrics for more rigorous evaluation. We demonstrate the effectiveness of M^3ashy through extensive experiments, including a novel statistics-based constrained synthesis, which enables the generation of materials of desired categories.
Paper Folding Puzzles: Can Multimodal Large Language Models Perform Spatial Reasoning?
Authors: Dibin Zhou; Yantao Xu; Zongming Huang; Zengwei Yan; Wenhao Liu; Yongwei Miao; Jianfeng Ren; Fuchang Liu
Venue: AAAI 2026 (main)

Abstract: Multimodal Large Language Models (MLLMs) largely lag human-level performance on abstract visual reasoning (AVR), which requires models to infer latent rules from visual question sets and generalize them to novel scenarios. Most AVR benchmarks are constrained to narrow and repetitive 2D patterns, involving relatively simple spatial relationships and assessing limited dimensions of reasoning ability. Drawing inspiration from real-world paper folding challenges, we propose Paper Folding Puzzles (PFP), a rigorously designed benchmark specifically developed to assess spatial reasoning capabilities. It comprises 150K visual question-answering samples across five diverse tasks, ranging from basic 2D geometric reasoning to 3D spatial understanding. The developed benchmark dataset can be employed to assess core spatial reasoning abilities essential to human cognition, encompassing fundamental symmetry reasoning and 3D spatial comprehension. Furthermore, we conduct a comprehensive evaluation of 18 leading MLLMs (both closed- and open-source variants) on the PFP benchmark to assess their spatial reasoning capabilities. Our findings show that most MLLMs achieve near-chance performance on FPF, exhibiting substantial performance gaps (>30%) relative to human baselines across all tasks. This highlights a critical research gap in improving spatial reasoning capabilities of MLLMs.
IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation
Authors: Donghao Zhou; Jingyu Lin; Guibao Shen; Quande Liu; Jialin Gao; Lihao Liu; Lan Du; Cunjian Chen; Chi-Wing Fu; Xiaowei Hu; Pheng-Ann Heng
Venue: AAAI 2026 (main)

Abstract: Recent visual generative models enable story generation with consistent characters from text, but human-centric story generation faces additional challenges, such as maintaining detailed and diverse human face consistency and coordinating multiple characters across different images. This paper presents IdentityStory, a framework for human-centric story generation that ensures consistent character identity across multiple sequential images. By taming identity-preserving generators, the framework features two key components: Iterative Identity Discovery, which extracts cohesive character identities, and Re-denoising Identity Injection, which re-denoises images to inject identities while preserving desired context. Experiments on the ConsiStory-Human benchmark demonstrate that IdentityStory outperforms existing methods, particularly in face consistency, and supports multi-character combinations. The framework also shows strong potential for applications such as infinite-length story generation and dynamic character composition.
Exploring Position Encoding Mechanism in Diffusion U-Net for Training-free High-resolution Image Generation
Authors: Feng Zhou; Pu Cao; Yiyang Ma; Lu Yang; Yonghao Dang; Jianqin Yin
Venue: AAAI 2026 (main)

Abstract: Denoising higher-resolution latents using a pre-trained U-Net often results in repetitive and disordered image patterns. In this work, we are motivated to reveal the intrinsic cause of such pattern disruption in high-resolution image generation. Through theoretical analysis and empirical studies, we reveal that the pre-trained U-Net fails to provide sufficient positional information for tokens at high-resolution. Specifically, 1) zero-padding serves as a critical mechanism for position encoding but lacks robustness across varying resolutions; and 2) tokens located farther from the feature map boundaries have increasing difficulty acquiring positional awareness, leading to pattern disruptions. Inspired by these findings, we propose a novel training-free approach for high-resolution generation, introducing a Progressive Boundary Complement (PBC) method. It creates dynamic virtual image boundaries inside the feature map to supplement position information at high resolution, enabling high-quality and rich-content high-resolution image synthesis. Extensive experiments show that our method significantly improves high-resolution image synthesis in terms of visual quality and content richness, achieving state-of-the-art performance.
Toward Real-World High-Precision Image Matting and Segmentation
Authors: Haipeng Zhou; Zhaohu Xing; Hongqiu Wang; Jun Ma; Ping Li; Lei Zhu
Venue: AAAI 2026 (main)

Abstract: High-precision scene parsing tasks, including image matting and dichotomous segmentation, aim to accurately predict masks with extremely fine details (such as hair). Most existing methods focus on salient, single foreground objects. While interactive methods allow for target adjustment, their class-agnostic design restricts generalization across different categories. Furthermore, the scarcity of high-quality annotation has led to a reliance on inharmonious synthetic data, resulting in poor generalization to real-world scenarios. To this end, we propose a Foreground Consistent Learning model, dubbed as FCLM, to address the aforementioned issues. Specifically, we first introduce a Depth-Aware Distillation strategy where we transfer the depth-related knowledge for better foreground representation. Considering the data dilemma, we term the processing of synthetic data as domain adaptation problem where we propose a domain-invariant learning strategy to focus on foreground learning. To support interactive prediction, we contribute an Object-Oriented Decoder that can receive both visual and language prompts to predict the referring target. Experimental results show that our method quantitatively and qualitatively outperforms state-of-the-art methods.
MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic Alignment
Authors: Hao Zhou; Xiaobao Guo; Yuzhe Zhu; Adams Wai-Kin Kong
Venue: AAAI 2026 (main)

Abstract: Propelled by the breakthrough in deep generative models, audio-to-image generation has emerged as a pivotal cross-modal task that converts complex auditory signals into rich visual representations. However, previous works only focus on single-source audio inputs for image generation, ignoring the multi-source characteristic in natural auditory scenes, thus limiting the performance in generating comprehensive visual content. To bridge this gap, we propose a method called MACS to conduct multi-source audio-to-image generation. To our best knowledge, this is the first work that explicitly separates multi-source audio to capture the rich audio components before image generation. MACS is a two-stage method. In the first stage, multi-source audio inputs are separated by a weakly supervised method, where the audio and text labels are semantically aligned by casting into a common space using the large pre-trained CLAP model. We introduce a ranking loss to consider the contextual significance of the separated audio signals. In the second stage, effective image generation is achieved by mapping the separated audio signals to the generation condition using only a trainable adapter and a MLP layer. We preprocess the LLP dataset as the first full multi-source audio-to-image generation benchmark. The experiments are conducted on multi-source, mixed-source, and single-source audio-to-image generation tasks. The proposed MACS outperforms the current state-of-the-art methods in 17 out of the 21 evaluation indexes on all tasks and delivers superior visual quality.
Duplex Rewards Optimization for Test-Time Composed Image Retrieval
Authors: Haoliang Zhou; Feifei Zhang; Changsheng Xu
Venue: AAAI 2026 (main)

Abstract: Composed Image Retrieval (CIR) combines the reference image with text to retrieve the intended target image. Recently, zero-shot CIR has gained significant attention by eliminating the need for labeled triplets required in supervised CIR. However, it inevitably demands additional training corpus, storage, and computational resources, limiting its applicability in real-world scenarios. Inspired by advancements in Test-Time Adaptation (TTA), we propose a Test-Time CIR setting named TT-CIR, which aims to efficiently adapt models to unlabeled test samples while reducing resource consumption. Within the TT-CIR setting, we identify that naively introducing existing TTA methods (e.g., reward-based) into CIR faces two vital challenges: 1) Modification-restricted reward pool, which limits the exploration of semantically relevant candidate rewards; 2) Conservative knowledge feedback, which inhibits the adaptability of rewards to the current data distribution. To address these challenges, we propose a test-time reinforcement learning framework that integrates a Counterfactual-guided Multinomial Sampling (CMS) strategy and a Duplex Rewards Modeling (DRM) module. The CMS explores a candidate reward pool that is visually similar and semantically relevant to the given query, while the DRM generates stable and adaptive duplex rewards to guide model adaptation. Extensive experiments demonstrate the superiority and adaptability of our method over existing approaches.
Seeing and Knowing in the Wild: Open-domain Visual Entity Recognition with Large-scale Knowledge Graphs via Contrastive Learning
Authors: Hongkuan Zhou; Lavdim Halilaj; Sebastian Monka; Stefan Schmid; Yuqicheng Zhu; Jingcheng Wu; Nadeem Nazer; Steffen Staab
Venue: AAAI 2026 (main)

Abstract: Open-domain visual entity recognition aims to identify and link entities depicted in images to a vast and evolving set of real-world concepts, such as those found in Wikidata. Unlike conventional classification tasks with fixed label sets, it operates under open-set conditions, where most target entities are unseen during training and exhibit long-tail distributions. This makes the task inherently challenging due to limited supervision, high visual ambiguity, and the need for semantic disambiguation. We propose a Knowledge-guided Contrastive Learning (KnowCoL) framework that combines both images and text descriptions into a shared semantic space grounded by structured information from Wikidata. By abstracting visual and textual inputs to a conceptual level, the model leverages entity descriptions, type hierarchies, and relational context to support zero-shot entity recognition.
We evaluate our approach on the OVEN benchmark, a large-scale open-domain visual recognition dataset with Wikidata IDs as the label space. Our experiments show that using visual, textual, and structured knowledge greatly improves accuracy, especially for rare and unseen entities. Our smallest model improves the accuracy on unseen entities by 10.5% compared to the state-of-the-art, despite being 35 times smaller.
T-Rex-Omni: Integrating Negative Visual Prompt in Generic Object Detection
Authors: Jiazhou Zhou; Qing Jiang; Kanghao Chen; Lutao Jiang; Yuanhuiyi Lyu; Ying-Cong Chen; Lei Zhang
Venue: AAAI 2026 (main)

Abstract: Object detection methods have evolved from closed-set to open-set paradigms over the years. Current open-set object detectors, however, remain constrained by their exclusive reliance on positive indicators based on given prompts like text descriptions or visual exemplars. This positive-only paradigm experiences consistent vulnerability to visually similar but semantically different distractors. We propose T-Rex-Omni, a novel framework that addresses this limitation by incorporating negative visual prompts to negate hard negative distractors. Specifically, we first introduce a unified visual prompt encoder that jointly processes positive and negative visual prompts. Next, a training-free Negating Negative Computing (NNC) module is proposed to dynamically suppress negative responses during the probability computing stage. To further boost performance through fine-tuning, our Negating Negative Hinge (NNH) loss enforces discriminative margins between positive and negative embeddings. T-Rex-Omni supports flexible deployment in both positive-only and joint positive-negative inference modes, accommodating either user-specified or automatically generated negative examples. Extensive experiments demonstrate remarkable zero-shot detection performance, significantly narrowing the performance gap between visual-prompted and text-prompted methods while showing particular strength in long-tailed scenarios (51.2 AP_r on LVIS-minival). This work establishes negative prompts as a crucial new dimension for advancing open-set visual recognition systems.
StrokeFusion: Vector Sketch Generation via Joint Stroke-UDF Encoding and Latent Sequence Diffusion
Authors: Jin Zhou; Yi Zhou; Hongliang Yang; Pengfei Xu; Hui Huang
Venue: AAAI 2026 (main)

Abstract: In the field of sketch generation, raster-format trained models often produce non-stroke artifacts, while vector-format trained models typically lack a holistic understanding of sketches, leading to compromised recognizability. Moreover, existing methods struggle to extract common features from similar elements (e.g., eyes of animals) appearing at varying positions across sketches. To address these challenges, we propose StrokeFusion, a two-stage framework for vector sketch generation. It contains a dual-modal sketch feature learning network that maps strokes into a high-quality latent space. This network decomposes sketches into normalized strokes and jointly encodes stroke sequences with Unsigned Distance Function (UDF) maps, representing sketches as sets of stroke feature vectors. Building upon this representation, our framework exploits a stroke-level latent diffusion model that simultaneously adjusts stroke position, scale, and trajectory during generation. This enables high-fidelity stroke generation while supporting stroke interpolation editing. Extensive experiments across multiple sketch datasets, demonstrate that our framework outperforms state-of-the-art techniques, validating its effectiveness in preserving structural integrity and semantic features.