GenAI4ELab/papercli-papers huggingface dataset:
Audio-Assisted Face Video Restoration with Temporal and Identity Complementary Learning
Authors: Yuqin Cao; Yixuan Gao; Wei Sun; Xiaohong Liu; Yulun Zhang; Xiongkuo Min
Venue: AAAI 2026 (main)

Abstract: Face videos accompanied by audio have become integral to our daily lives, while they often suffer from complex degradations. Most face video restoration methods neglect the intrinsic correlations between visual and audio features, particularly in the mouth region. Several audio-aided face video restoration methods have been proposed, but they only focus on compression artifact removal. In this paper, we propose a General Audio-assisted face Video restoration Network (GAVN) to address various types of streaming video distortions via identity and temporal complementary learning. Specifically, GAVN first captures inter-frame temporal features in the low-resolution space to restore frames coarsely and save computational cost. Then, GAVN extracts intra-frame identity features in the high-resolution space with the assistance of audio signals and face landmarks to restore more facial details. Finally, the reconstruction module integrates temporal features and identity features to generate high-quality face videos. Experimental results demonstrate that GAVN outperforms the existing state-of-the-art methods on face video compression artifact removal, deblurring, and super-resolution.
Multivariate Diffusion Transformer with Decoupled Attention for High-Fidelity Mask-Text Collaborative Facial Generation
Authors: Yushe Cao; Dianxi Shi; Xing Fu; Xuechao Zou; Haikuo Peng; Xueqi Li; Chun Yu; Junliang Xing
Venue: AAAI 2026 (main)

Abstract: While significant progress has been achieved in multimodal facial generation using semantic masks and textual descriptions, conventional feature fusion approaches often fail to enable effective cross-modal interactions, thereby leading to suboptimal generation outcomes. To address this challenge, we introduce MDiTFace—a customized diffusion transformer framework that employs a unified tokenization strategy to process semantic mask and text inputs, eliminating discrepancies between heterogeneous modality representations. The framework facilitates comprehensive multimodal feature interaction through stacked, newly designed multivariate transformer blocks that process all conditions synchronously. Additionally, we design a novel decoupled attention mechanism by dissociating implicit dependencies between mask tokens and temporal embeddings. This mechanism segregates internal computations into dynamic and static pathways, enabling caching and reuse of features computed in static pathways after initial calculation, thereby  reducing additional computational overhead introduced by mask condition by over 94% while maintaining performance. Extensive experiments demonstrate that MDiTFace significantly outperforms other competing methods in terms of both facial fidelity and conditional consistency.
Maniflat3D: Learning 3D Geometry Through Planar Representations from Multi-Layer Unwrapping
Authors: Zijian Cao; Dayou Zhang; Zeyuan Liu; Zhicheng Liang; Fangxin Wang
Venue: AAAI 2026 (main)

Abstract: Point-based geometric representations such as point clouds and Gaussian Splatting are fundamental for 3D understanding. However, the inherent irregularity and high-dimensional nature of point structures present significant challenges for direct 3D learning approaches, which often struggle with scalability and achieve suboptimal performance due to sparse data distributions. In contrast, 2D learning paradigms benefit from well-established architectures with superior optimization stability and efficiency. To bridge this gap, we propose Maniflat3D, a unified framework that systematically transforms volumetric point-based geometries into structured 2D representations through a two-stage process: a multilayer Ball-Pivoting reconstruction with adaptive density control, followed by Scalable Locally Injective Mapping (SLIM) to produce distortion-minimized, bijective UV parameterizations.
Our approach explicitly encodes both geometric and attribute information into the flattened domain, enabling conventional 2D neural networks to effectively learn from complex 3D structures such as Gaussian Splatting. Experiments on the ShapeSplat dataset demonstrate that Maniflat3D achieves comparable performance while reducing parameter count by 90% compared to native 3D baselines, and simultaneously attains 21× compression ratio through neural encoding. These results establish a new paradigm for efficient geometric understanding, demonstrating successful transfer of planar learning advantages to challenging 3D manifold problems through dimensional reduction.
QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution
Authors: Bowen Chai; Zheng Chen; Libo Zhu; Wenbo Li; Yong Guo; Yulun Zhang
Venue: AAAI 2026 (main)

Abstract: Diffusion models have shown superior performance in real-world video super-resolution (VSR). However, the slow processing speeds and heavy resource consumption of diffusion models hinder their practical application and deployment. Quantization offers a potential solution for compressing the VSR model. Nevertheless, quantizing VSR models is challenging due to their temporal characteristics and high fidelity requirements. To address these issues, we propose QuantVSR, a low-bit quantization model for real-world VSR. We propose a spatio-temporal complexity aware (STCA) mechanism, where we first utilize the calibration dataset to measure both spatial and temporal complexities for each layer. Based on these statistics, we allocate layer-specific ranks to the low-rank full-precision (FP) auxiliary branch. Subsequently, we jointly refine the FP and low-bit branches to achieve simultaneous optimization. In addition, we propose a learnable bias alignment (LBA) module to reduce the biased quantization errors. Extensive experiments on synthetic and real-world datasets demonstrate that our method obtains comparable performance with the FP model and significantly outperforms recent leading low-bit quantization methods.
AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs
Authors: Boyu Chang; Qi Wang; Xi Guo; Zhixiong Nan; Yazhou Yao; Tianfei Zhou
Venue: AAAI 2026 (main)

Abstract: Visual abductive reasoning (VAR) is a challenging task that requires AI systems to infer the most likely explanation for incomplete visual observations. While recent MLLMs develop strong general-purpose multimodal reasoning capabilities, they remain fall short in abductive inference, as compared to human beings. To bridge this gap, we draw inspiration from the interplay between verbal and pictorial abduction in human cognition, and propose to strengthen abduction of MLLMs by mimicking such dual-mode behavior.  Concretely, we introduce AbductiveMLLM comprising of two synergistic components:  REASONER and IMAGINER. The REASONER operates in the verbal domain. It first explores a broad space of possible explanations using a blind LLM and then prunes visually incongruent hypotheses based on cross-modal causal alignment. The remaining hypotheses are introduced into the MLLM as targeted priors, steering its reasoning toward causally coherent explanations. The IMAGINER, on the other hand, further guides MLLMs by emulating human-like pictorial thinking. It conditions a text-to-image diffusion model on both the input video and the REASONER’s output embeddings to “imagine” plausible visual scenes that correspond to verbal explanation, thereby enriching MLLMs' contextual grounding. The two components are trained jointly in an end-to-end manner. Experiments on standard VAR benchmarks show that AbductiveMLLM achieves state-of-the-art performance, consistently outperforming traditional solutions and advanced MLLMs.
Reconstruction Using the Invisible: Intuition from NIR and Metadata for Enhanced 3D Gaussian Splatting
Authors: Gyusam Chang; Tuan-Anh Vu; Vivek Alumootil; Harris Song; Deanna Pham; Sangpil Kim; M. Khalid Jawed
Venue: AAAI 2026 (main)

Abstract: While 3D Gaussian Splatting (3DGS) has rapidly advanced, its application in agriculture remains underexplored. Agricultural scenes pose unique challenges for 3D reconstruction methods, notably uneven illumination, occlusions, and limited perspectives. To address these limitations, we introduce NTRPlant, a novel multimodal dataset encompassing Near-Infrared (NIR), RGB imagery, textual metadata, Depth, and LiDAR collected under varied indoor and outdoor lighting conditions. By integrating NIR data, our approach enhances robustness and extracts crucial botanical insights beyond visible spectra. Additionally, we leverage text-based metadata derived from vegetation indices, such as NDVI, NDWI, and chlorophyll index, significantly enriching the contextual understanding of complex agricultural environments. To fully exploit these modalities, we propose NIRSplat, an effective multimodal Gaussian splatting architecture employing a cross-attention mechanism combined with 3D point-based positional encoding, providing robust geometric priors. Comprehensive experiments demonstrate that NIRSplat outperforms existing state-of-the-art methods, including 3DGS and InstantSplat, highlighting its effectiveness in challenging agricultural scenarios.
MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting
Authors: Hanzhi Chang; Ruijie Zhu; Wenjie Chang; Mulin Yu; Yanzhe Liang; Jiahao Lu; Zhuoyuan Li; Tianzhu Zhang
Venue: AAAI 2026 (main)

Abstract: Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks.
MambaOVSR: Multiscale Fusion with Global Motion Modeling for Chinese Opera Video Super-Resolution
Authors: Hua Chang; Xin Xu; Wei Liu; Wei Wang; Xin Yuan; Kui Jiang
Venue: AAAI 2026 (main)

Abstract: Chinese opera is celebrated for preserving classical art. However, early filming equipment limitations have degraded videos of last-century performances by renowned artists (e.g., low frame rates and resolution), hindering archival efforts. Although space-time video super-resolution (STVSR) has advanced significantly, applying it directly to opera videos remains challenging. The scarcity of datasets impedes the recovery of high-frequency details, and existing STVSR methods lack global modeling capabilities—compromising visual quality when handling opera’s characteristic large motions. To address these challenges, we pioneer a large-scale Chinese Opera Video Clip (COVC) dataset and propose the Mamba-based multiscale fusion network for space-time Opera Video Super-Resolution (MambaOVSR). Specifically, MambaOVSR involves three novel components: the Global Fusion Module (GFM) for motion modeling through a multiscale alternating scanning mechanism, and the Multiscale Synergistic Mamba Module (MSMM) for alignment across different sequence lengths. Additionally, our MambaVR block resolves feature artifacts and positional information loss during alignment. Experimental results on the COVC dataset show that MambaOVSR significantly outperforms the SOTA STVSR method by an average of 1.86 dB in terms of PSNR.
Escaping the CAM Shadow: Uncertainty-Guided Reliable Learning for Weakly Supervised Semantic Segmentation
Authors: Luyao Chang; Leiting Chen; Chen Yang; Chuan Zhou
Venue: AAAI 2026 (main)

Abstract: Weakly supervised semantic segmentation (WSSS) suffers from an inherent mismatch between coarse image-level annotations and dense pixel-level predictions. To bridge this gap, existing methods primarily focus on generating refined class activation maps (CAM) as pseudo-labels. However, we argue that this focus is insufficient as it overlooks a critical component: the segmentation decoder. The decoder is typically trained through superficial alignment of predictions with pseudo-labels in the logit space. Given the noisy nature of such labels, this naive supervision leads to error accumulation and limits performance. To address this, we propose an Uncertainty-Guided Reliable Learning (UGRL) framework that exerts dual control to reshape the learning process, achieving robust supervision that escapes the CAM shadow. The cornerstone of UGRL is a prototype-driven uncertainty modeling module that estimates the reliability of class-wise supervision. The modeled uncertainty enables two synergistic control mechanisms. First, it adaptively modulates classification and segmentation losses, encouraging the model to learn from more trustworthy signals. Second, it guides the structuring of the decoder’s feature space. Rather than relying solely on superficial alignment, UGRL enforces deeper representation alignment by applying contrastive learning on reliable pixels. This enables rich semantic transfer to fine-grained segmentation details. Extensive experiments on PASCAL VOC and MS COCO demonstrate that our method surpasses other state-of-the-art WSSS methods.
BulletTime4D: Towards High Spatio-Temporal Resolution Dynamic Scene Rendering via Spike-Guided Stereo Vision
Authors: Yiqian Chang; Haoran Xu; Qinghong Ye; Jianing Li; Xuan Wang; Wei Zhang; Peixi Peng
Venue: AAAI 2026 (main)

Abstract: High spatio‑temporal resolution novel‑view scene rendering is crucial for applications such as sports analysis and scientific experiments. However, existing Dynamic Scene Rendering (DSR) approaches typically rely on conventional RGB cameras with limited frame rates, making it difficult to achieve high spatio‑temporal resolution. In this paper, we present BulletTime4D, a high spatio‑temporal resolution DSR framework, which is the first trial to integrate a spike camera with binocular RGB cameras for dynamic scene reconstruction. Specifically, we first develop a hybrid camera prototype and build a real‑world dynamic scene reconstruction dataset. Then, BulletTime4D presents a multi‑timescale deformation representation by combining low‑frequency spatio‑temporal features with high‑frequency inter‑frame motion features. Finally, a rendering network is designed capable of projecting 4D Gaussians into the spike domain for spike rendering, and a cross‑domain supervision strategy is proposed to achieve high‑frame‑rate texture and color rendering. The results show that BulletTime4D outperforms state‑of‑the‑art methods on both simulated and real‑world datasets. In addition, BulletTime4D can synthesize 300 FPS novel‑view renderings using stereo RGB cameras at 30 FPS and a single spike camera.
PerTouch: VLM-Driven Agent for Personalized and Semantic Image Retouching
Authors: Zewei Chang; Zheng-Peng Duan; Jianxing Zhang; Chun-Le Guo; Siyu Liu; Hyungju Chun; Hyunhee Park; Zikun Liu; Chongyi Li
Venue: AAAI 2026 (main)

Abstract: Image retouching aims to enhance visual quality while aligning with users' personalized aesthetic preferences. To address the challenge of balancing controllability and subjectivity, we propose a unified diffusion-based image retouching framework called PerTouch. Our method supports semantic-level image retouching while maintaining global aesthetics. Using parameter maps containing attribute values in specific semantic regions as input, PerTouch constructs an explicit parameter-to-image mapping for fine-grained image retouching. To improve semantic boundary perception, we introduce semantic replacement and parameter perturbation mechanisms during training. To connect natural language instructions with visual control, we develop a VLM-driven agent to handle both strong and weak user instructions. Equipped with mechanisms of feedback-driven rethinking and scene-aware memory, PerTouch better aligns with user intent and captures long-term preferences. Extensive experiments demonstrate each component’s effectiveness and the superior performance of PerTouch in personalized image retouching.
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment
Authors: Baoliang Chen; Siyi Pan; Dongxu Wu; Liang Xie; Xiangjie Sui; Lingyu Zhu; Hanwei Zhu
Venue: AAAI 2026 (main)

Abstract: Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason is that LMMs are primarily trained for high-level tasks (e.g., image captioning), emphasizing unified image semantics extraction under varied quality. Such semantic-aware yet quality-insensitive perception bias inevitably leads to a heavy reliance on image semantics when those LMMs are forced for quality rating. In this paper, instead of retraining or tuning an LMM costly, we propose a training-free debiasing framework, in which the image quality prediction is rectified by mitigating the bias caused by image semantics. Specifically, we first explore several semantic-preserving distortions that can significantly degrade image quality while maintaining identifiable semantics. By applying these specific distortions to the query/test images, we ensure that the degraded images are recognized as poor quality while their semantics remain. During quality inference, both a query image and its corresponding degraded version are fed to the LMM along with a prompt indicating that the query image quality should be inferred under the condition that the degraded one is deemed poor quality. This prior condition effectively aligns the LMM’s quality perception, as all degraded images are consistently rated as poor quality, regardless of their semantic difference. Finally, the quality scores of the query image inferred under different prior conditions (degraded versions) are aggregated using a conditional probability model. Extensive experiments on various IQA datasets show that our debiasing framework could consistently enhance the LMM performance and the code will be publicly available.
Style4D-Bench: A Benchmark Suite for 4D Stylization
Authors: Beiqi Chen; Shuai Shao; Haitang Feng; Jianhuang Lai; Jianlou Si; Guangcong Wang
Venue: AAAI 2026 (main)

Abstract: We introduce Style4D-Bench, the first benchmark suite specifically designed for 4D stylization, with the goal of standardizing evaluation and facilitating progress in this emerging area. Style4D-Bench comprises: 1) a strong baseline that make an initial attempt for 4D stylization, 2) a comprehensive evaluation protocol measuring spatial fidelity, temporal coherence, and multi-view consistency through both perceptual and quantitative metrics, and 3) a curated collection of high-resolution dynamic 4D scenes with diverse motions and complex backgrounds. To establish a strong baseline, we present Style4D, a novel framework built upon 4D Gaussian Splatting. It consists of three key components: a basic 4DGS scene representation to capture reliable geometry, a Style Gaussian Representation that leverages lightweight per-Gaussian MLPs for temporally and spatially aware appearance control, and a Holistic Geometry-Preserved Style Transfer module designed to enhance spatio-temporal consistency via contrastive coherence learning and structural content preservation. Extensive experiments on Style4D-Bench demonstrate that Style4D achieves state-of-the-art performance in 4D stylization, producing fine-grained stylistic details with stable temporal dynamics and consistent multi-view rendering. We expect Style4D-Bench to become a valuable resource for benchmarking and advancing research in stylized rendering of dynamic 3D scenes.
Towards Ultrasound-based Reliable Disease Diagnosis Using Causal Inference
Authors: Bolei Chen; Jiaxu Kang; Haonan Yang; Ping Zhong; Yixiong Liang; Rui Fan; Jianxin Wang
Venue: AAAI 2026 (main)

Abstract: Aligning the decision-making process of deep learning models with that of experienced sonographers is essential for ultrasound-based reliable disease diagnosis. Although existing methods have made significant progress in this aspect, their alignments are primarily associational rather than causal, leading to pseudo-correlations between features and diagnostic results. Such a biased diagnosis blindly models the sonographer's diagnostic skills and attention to specific patterns, which we argue hardly produces an AI diagnoser that is comparable to human experts. To address this issue, we propose a causality-based diagnostic framework to align the model's diagnostic behaviors with those of experts. Specifically, by delving into both conspicuous and inconspicuous confounders within the ultrasound images, the back-door and front-door adjustment causal learning modules are proposed to promote unbiased learning by mitigating potential pseudo-correlations. In addition, we integrate causal inference into a well-designed dual-branch model with feature interaction bridges for compatibility with multimodal ultrasound inputs. To fully evaluate our method, we conduct comparative studies on different diseases and ultrasound modalities. In particular, we publish a carefully constructed multimodal ultrasound dataset for breast lesion diagnosis and segmentation. Sufficient comparative and ablation studies on this dataset emphasize that our method outperforms state-of-the-art methods.
Perspective from a Broader Context: Can Room Style Knowledge Help Visual Floorplan Localization?
Authors: Bolei Chen; Shengsheng Yan; Yongzheng Cui; Jiaxu Kang; Ping Zhong; Jianxin Wang
Venue: AAAI 2026 (main)

Abstract: Since a building's floorplan remains consistent over time and is inherently robust to changes in visual appearance, visual Floorplan Localization (FLoc) has received increasing attention from researchers. However, as a compact and minimalist representation of the building's layout, floorplans contain many repetitive structures (e.g., hallways and corners), thus easily result in ambiguous localization. Existing methods either pin their hopes on matching 2D structural cues in floorplans or rely on 3D geometry-constrained visual pre-trainings, ignoring the richer contextual information provided by visual images. In this paper, we suggest using broader visual scene context to empower FLoc algorithms with scene layout priors to eliminate localization uncertainty. In particular, we propose an unsupervised learning technique with clustering constraints to pre-train a room discriminator on self-collected unlabeled room images. Such a discriminator can empirically extract the hidden room type of the observed image and distinguish it from other room types. By injecting the scene context information summarized by the discriminator into an FLoc algorithm, the room style knowledge is effectively exploited to guide definite visual FLoc. We conducted sufficient comparative studies on two standard visual Floc benchmarks. Our experiments show that our approach outperforms state-of-the-art methods and achieves significant improvements in robustness and accuracy.