PanoPlane achieves up to 17.8% PSNR gains in sparse-view indoor novel view synthesis by using training-free plane-aware panoramic completion to supervise 3D Gaussian Splatting.
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Bovik, Hamid R
Canonical reference. 73% of citing Pith papers cite this work as background.
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representative citing papers
GuardMarkGS unifies watermarking and adversarial edit deterrence into a single optimization framework for protecting 3D Gaussian Splatting assets.
A new large-scale synthetic multi-task benchmark dataset supplying pixel-perfect depth, domain-shifted night imagery, and multi-scale low-resolution pairs for aerial remote sensing.
MESA restores ancient inscription textures via multi-exemplar style transfer from VGG19 features with per-layer exemplar selection and OCR-derived weights, without any model training.
GeRM learns a distribution transfer vector field via a multi-condition ControlNet to convert physically-based renders into photorealistic images using text prompts and a 50K expert-curated dataset.
LumaFlux is a physically and perceptually guided diffusion transformer for SDR-to-HDR conversion that introduces PGA, PCM, and HDR Residual Coupler modules plus a new training corpus and benchmark, outperforming prior ITM methods.
A sensor-specific calibration pipeline using dark frames produces synthesized noisy RAW images that close 54-64% of the PSNR gap to real noise versus manufacturer profiles, accompanied by the open SNIC dataset of over 6600 paired images.
DRFS is a new inversion-free editing technique for rectified flow models that models source-target velocity discrepancies and applies a time-dependent shift to improve fidelity and unify prior methods like DDS and FlowEdit.
Harder classification tasks produce neural representations whose accuracy collapses under binarization and shuffling while easier tasks remain robust, defining task complexity via the performance gap between full-precision and perturbed networks.
PhotIQA is a new public dataset of 1134 expert-rated photoacoustic images for benchmarking image quality assessment in medical imaging.
Presents SLAM&Render, a robot-recorded benchmark dataset with 40 multi-modal sequences for testing SLAM, novel view synthesis, and Gaussian Splatting under controlled variations in lighting, arrangements, and occlusions.
Proposes a cyclic 2.5D perceptual loss with manufacturer SUVR standardization for T1w MRI to tau PET synthesis, reporting improved regional agreement on ADNI and SCAN cohorts across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.
LiFT factorizes 3D medical volume synthesis into per-slice 2D generation and inter-slice trajectory learning, using a tri-planar drifting loss for unconditional coherence and a z-context mixer for paired translation tasks.
MSIQ is a scale-invariant, model-free quality metric for single image super-resolution using normalized central geometric moments for direct comparison of different-resolution images.
MIRAGE achieves state-of-the-art mental image reconstruction from fMRI on the NSD-Imagery benchmark by using a linear backbone with multi-modal text and image features fed to a diffusion model.
Joint training of NCA rules and SIREN pre-patterns improves robustness, encoding capacity, and symmetry breaking compared to purely self-organizing models by offloading information to initial conditions.
LiBrA-Net achieves real-time native 4K video dehazing via Lie-algebraic bilateral affine fields and releases the first 4K paired dehazing video benchmark with per-frame annotations.
Linear mappings in feature space can reconstruct a wide range of image manipulations including semantic edits, suggesting that feature representations are approximately linearly organized.
AsyncEvGS reconstructs high-fidelity 3D scenes from motion-blurred images by first deblurring via event data then using VGGT-based pose estimation and structure-driven losses inside Gaussian Splatting.
SIFT-VTON adds explicit geometric supervision from SIFT keypoints to diffusion-based virtual try-on to improve spatial alignment and detail preservation.
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
Proposes TinyUSFM-uLPIPS and TinyUSFM-NRQ metrics that show better alignment with segmentation task performance and expert preference than PSNR or VGG-LPIPS in ultrasound imaging.
CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.
citing papers explorer
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FeatMap: Understanding image manipulation in the feature space and its implications for feature space geometry
Linear mappings in feature space can reconstruct a wide range of image manipulations including semantic edits, suggesting that feature representations are approximately linearly organized.
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CoCoDiff: Optimizing Collective Communications for Distributed Diffusion Transformer Inference Under Ulysses Sequence Parallelism
CoCoDiff achieves 3.6x average and 8.4x peak speedup for distributed DiT inference on up to 96 GPU tiles via tile-aware all-to-all, V-first scheduling, and selective V communication.
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PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios
PostureObjectStitch generates assembly-aware anomaly images by decoupling multi-view features into high-frequency, texture and RGB components, modulating them temporally in a diffusion model, and applying conditional loss plus geometric priors to preserve correct component relationships.
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ReplicateAnyScene: Zero-Shot Video-to-3D Composition via Textual-Visual-Spatial Alignment
ReplicateAnyScene performs fully automated zero-shot video-to-compositional-3D reconstruction by cascading alignments of generic priors from vision foundation models across textual, visual, and spatial dimensions.
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SyncBreaker:Stage-Aware Multimodal Adversarial Attacks on Audio-Driven Talking Head Generation
SyncBreaker jointly attacks image and audio streams with Multi-Interval Sampling and Cross-Attention Fooling to degrade speech-driven talking head generation more than single-modality baselines.
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Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering
A neural network trained on full-reference perceptual quality labels predicts minimal sufficient resolution for rendered video to enable power-efficient client-side rendering.
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UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
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MSDS: Deep Structural Similarity with Multiscale Representation
MSDS computes DeepSSIM at multiple pyramid scales and fuses the scores with learned weights, producing consistent improvements over single-scale DeepSSIM on IQA benchmarks with negligible extra cost.