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Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So?
arXiv:2606.05478v1 Announce Type: new Abstract: Diffusion Models (DM) have revolutionized text-driven generation by enabling the synthesis of high-quality, photorealistic visual content from user prompts. Whereas prior advances in visual generation such as VAEs and GANs were primarily evaluated on perceptual or visual similarity metrics such as FID PSNR, DM advances have fostered the development of more advanced Human Preference Metrics (HPM) that model and quantify human judgment as scalar...
Wavelet as Tokenizer: Preliminary Results on a Shared Wavelet Token Schema for Natural Signals
arXiv:2606.02631v1 Announce Type: cross Abstract: This paper studies whether audio, images, and video can share a common wavelet token schema rather than relying on separate modality-specific latent grids. It introduces a preliminary continuous-token model built around a one-level Haar DWT/IDWT frontend, a shared coefficient-token layout, optional structural metadata, lightweight modality value adapters, and a shared token-wise encoder-decoder trunk. On Speech Commands, EuroSAT RGB, and...
LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss
Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. However, its standard photometric loss treats flat and structure-rich regions similarly, which may limit the recovery of sharp contours and fine details. Edge-Guided Gaussian Splatting (EGGS) improves structure awareness through edge-guided weighting, but mainly relies on first-order gradient responses and linear...
KC-3DGS: Kurtosis-Constrained Gaussian Splatting for High-Fidelity View Synthesis
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Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
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CFRNet: Cycle-Consistent Fixed-Point Training for Real-Time Blind Face Restoration on Consumer Embedded NPUs
arXiv:2606.06850v1 Announce Type: new Abstract: Blind face restoration on consumer devices has to balance image quality against speed and memory. Strong methods such as GFPGAN and CodeFormer give good perceptual quality, but they rely on large pretrained generative priors and on operators such as attention, codebook lookup, and style modulation that are hard to compile and quantize on the small neural processing units (NPUs) used in consumer hardware. Small convolutional restorers run fast...
Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography
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An Attention-Based Denoising Model for Diffusion Weighted Imaging
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End-to-End Inverse Designed Single-Layered Metasurface for Snapshot RGB-Achromatic Full-Stokes Polarization Imaging
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Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates
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