MetricHMR: Metric Human Mesh Recovery from Monocular Images

We introduce MetricHMR (Metric Human Mesh Recovery), an approach for metric human mesh recovery with accurate global translation from monocular images. In contrast to existing HMR methods that suffer from severe scale and depth ambiguity, MetricHMR is able to produce geometrically reasonable body shape and global translation in the reconstruction results. To this end, we first systematically analyze previous HMR methods on camera models to emphasize the critical role of the standard perspective projection model in enabling metric-scale HMR. We then validate the acceptable ambiguity range of metric HMR under the standard perspective projection model. Finally, we contribute a novel approach that introduces a ray map based on the standard perspective projection to jointly encode bounding-box information, camera parameters, and geometric cues for End2End metric HMR without any additional metric-regularization modules. Extensive experiments demonstrate that our method achieves state-of-the-art performance, even compared with sequential HMR methods, in metric pose, shape, and global translation estimation across both indoor and in-the-wild scenarios.

Motion-Aware Adaptive Pixel Pruning for Efficient Local Motion Deblurring

Local motion blur in digital images originates from the relative motion between dynamic objects and static imaging systems during exposure. Existing deblurring methods face significant challenges in addressing this problem due to their inefficient allocation of computational resources and inadequate handling of spatially varying blur patterns. To overcome these limitations, we first propose a trainable mask predictor that identifies blurred regions in the image. During training, we employ blur masks to exclude sharp regions. For inference optimization, we implement structural reparameterization by converting $3\times 3$ convolutions to computationally efficient $1\times 1$ convolutions, enabling pixel-level pruning of sharp areas to reduce computation. Second, we develop an intra-frame motion analyzer that translates relative pixel displacements into motion trajectories, establishing adaptive guidance for region-specific blur restoration. Our method is trained end-to-end using a combination of reconstruction loss, reblur loss, and mask loss guided by annotated blur masks. Extensive experiments demonstrate superior performance over state-of-the-art methods on both local and global blur datasets while reducing FLOPs by 49\% compared to SOTA models (e.g., LMD-ViT). The source code is available at https://github.com/shangwei5/M2AENet.

Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures

Purpose: Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning.While multi-inversion time (multi-TI) T$_1$-weighted (T$_1$-w) magnetic resonance (MR) imaging improves visualization, it is rarely acquired in clinical settings. Approach: We present SyMTIC (Synthetic Multi-TI Contrasts), a deep learning method that generates synthetic multi-TI images using routinely acquired T$_1$-w, T$_2$-weighted (T$_2$-w), and FLAIR images. Our approach combines image translation via deep neural networks with imaging physics to estimate longitudinal relaxation time (T$_1$) and proton density (PD) maps. These maps are then used to compute multi-TI images with arbitrary inversion times. Results: SyMTIC was trained using paired MPRAGE and FGATIR images along with T$_2$-w and FLAIR images. It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data.The synthetic images, especially for TI values between 400-800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei. Conclusion: SyMTIC enables robust generation of high-quality multi-TI images from routine MR contrasts. It generalizes well to varied clinical datasets, including those with missing FLAIR images or unknown parameters, offering a practical solution for improving brain MR image visualization and analysis.

APTOS-2024 challenge report: Generation of synthetic 3D OCT images from fundus photographs

Optical Coherence Tomography (OCT) provides high-resolution, 3D, and non-invasive visualization of retinal layers in vivo, serving as a critical tool for lesion localization and disease diagnosis. However, its widespread adoption is limited by equipment costs and the need for specialized operators. In comparison, 2D color fundus photography offers faster acquisition and greater accessibility with less dependence on expensive devices. Although generative artificial intelligence has demonstrated promising results in medical image synthesis, translating 2D fundus images into 3D OCT images presents unique challenges due to inherent differences in data dimensionality and biological information between modalities. To advance generative models in the fundus-to-3D-OCT setting, the Asia Pacific Tele-Ophthalmology Society (APTOS-2024) organized a challenge titled Artificial Intelligence-based OCT Generation from Fundus Images. This paper details the challenge framework (referred to as APTOS-2024 Challenge), including: the benchmark dataset, evaluation methodology featuring two fidelity metrics-image-based distance (pixel-level OCT B-scan similarity) and video-based distance (semantic-level volumetric consistency), and analysis of top-performing solutions. The challenge attracted 342 participating teams, with 42 preliminary submissions and 9 finalists. Leading methodologies incorporated innovations in hybrid data preprocessing or augmentation (cross-modality collaborative paradigms), pre-training on external ophthalmic imaging datasets, integration of vision foundation models, and model architecture improvement. The APTOS-2024 Challenge is the first benchmark demonstrating the feasibility of fundus-to-3D-OCT synthesis as a potential solution for improving ophthalmic care accessibility in under-resourced healthcare settings, while helping to expedite medical research and clinical applications.

SPC to 3D: Novel View Synthesis from Binary SPC via I2I translation

Single Photon Avalanche Diodes (SPADs) represent a cutting-edge imaging technology, capable of detecting individual photons with remarkable timing precision. Building on this sensitivity, Single Photon Cameras (SPCs) enable image capture at exceptionally high speeds under both low and high illumination. Enabling 3D reconstruction and radiance field recovery from such SPC data holds significant promise. However, the binary nature of SPC images leads to severe information loss, particularly in texture and color, making traditional 3D synthesis techniques ineffective. To address this challenge, we propose a modular two-stage framework that converts binary SPC images into high-quality colorized novel views. The first stage performs image-to-image (I2I) translation using generative models such as Pix2PixHD, converting binary SPC inputs into plausible RGB representations. The second stage employs 3D scene reconstruction techniques like Neural Radiance Fields (NeRF) or Gaussian Splatting (3DGS) to generate novel views. We validate our two-stage pipeline (Pix2PixHD + Nerf/3DGS) through extensive qualitative and quantitative experiments, demonstrating significant improvements in perceptual quality and geometric consistency over the alternative baseline.

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

In recent years, large language models (LLMs) have transformed natural language understanding through vast datasets and large-scale parameterization. Inspired by this success, we present SpecCLIP, a foundation model framework that extends LLM-inspired methodologies to stellar spectral analysis. Stellar spectra, akin to structured language, encode rich physical and chemical information about stars. By training foundation models on large-scale spectral datasets, our goal is to learn robust and informative embeddings that support diverse downstream applications. As a proof of concept, SpecCLIP involves pre-training on two spectral types--LAMOST low-resolution and Gaia XP--followed by contrastive alignment using the CLIP (Contrastive Language-Image Pre-training) framework, adapted to associate spectra from different instruments. This alignment is complemented by auxiliary decoders that preserve spectrum-specific information and enable translation (prediction) between spectral types, with the former achieved by maximizing mutual information between embeddings and input spectra. The result is a cross-spectrum framework enabling intrinsic calibration and flexible applications across instruments. We demonstrate that fine-tuning these models on moderate-sized labeled datasets improves adaptability to tasks such as stellar-parameter estimation and chemical-abundance determination. SpecCLIP also enhances the accuracy and precision of parameter estimates benchmarked against external survey data. Additionally, its similarity search and cross-spectrum prediction capabilities offer potential for anomaly detection. Our results suggest that contrastively trained foundation models enriched with spectrum-aware decoders can advance precision stellar spectroscopy.

Moment kernels: a simple and scalable approach for equivariance to rotations and reflections in deep convolutional networks

The principle of translation equivariance (if an input image is translated an output image should be translated by the same amount), led to the development of convolutional neural networks that revolutionized machine vision. Other symmetries, like rotations and reflections, play a similarly critical role, especially in biomedical image analysis, but exploiting these symmetries has not seen wide adoption. We hypothesize that this is partially due to the mathematical complexity of methods used to exploit these symmetries, which often rely on representation theory, a bespoke concept in differential geometry and group theory. In this work, we show that the same equivariance can be achieved using a simple form of convolution kernels that we call ``moment kernels,'' and prove that all equivariant kernels must take this form. These are a set of radially symmetric functions of a spatial position $x$, multiplied by powers of the components of $x$ or the identity matrix. We implement equivariant neural networks using standard convolution modules, and provide architectures to execute several biomedical image analysis tasks that depend on equivariance principles: classification (outputs are invariant under orthogonal transforms), 3D image registration (outputs transform like a vector), and cell segmentation (quadratic forms defining ellipses transform like a matrix).

ImplicitStainer: Data-Efficient Medical Image Translation for Virtual Antibody-based Tissue Staining Using Local Implicit Functions

Hematoxylin and eosin (H&E) staining is a gold standard for microscopic diagnosis in pathology. However, H&E staining does not capture all the diagnostic information that may be needed. To obtain additional molecular information, immunohistochemical (IHC) stains highlight proteins that mark specific cell types, such as CD3 for T-cells or CK8/18 for epithelial cells. While IHC stains are vital for prognosis and treatment guidance, they are typically only available at specialized centers and time consuming to acquire, leading to treatment delays for patients. Virtual staining, enabled by deep learning-based image translation models, provides a promising alternative by computationally generating IHC stains from H&E stained images. Although many GAN and diffusion based image to image (I2I) translation methods have been used for virtual staining, these models treat image patches as independent data points, which results in increased and more diverse data requirements for effective generation. We present ImplicitStainer, a novel approach that leverages local implicit functions to improve image translation, specifically virtual staining performance, by focusing on pixel-level predictions. This method enhances robustness to variations in dataset sizes, delivering high-quality results even with limited data. We validate our approach on two datasets using a comprehensive set of metrics and benchmark it against over fifteen state-of-the-art GAN- and diffusion based models. Full Code and models trained will be released publicly via Github upon acceptance.

LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping

In this study, we introduce LoopDB, which is a challenging loop closure dataset comprising over 1000 images captured across diverse environments, including parks, indoor scenes, parking spaces, as well as centered around individual objects. Each scene is represented by a sequence of five consecutive images. The dataset was collected using a high resolution camera, providing suitable imagery for benchmarking the accuracy of loop closure algorithms, typically used in simultaneous localization and mapping. As ground truth information, we provide computed rotations and translations between each consecutive images. Additional to its benchmarking goal, the dataset can be used to train and fine-tune loop closure methods based on deep neural networks. LoopDB is publicly available at https://github.com/RovisLab/LoopDB.

Towards Generating Realistic Underwater Images

This paper explores the use of contrastive learning and generative adversarial networks for generating realistic underwater images from synthetic images with uniform lighting. We investigate the performance of image translation models for generating realistic underwater images using the VAROS dataset. Two key evaluation metrics, Fr\'echet Inception Distance (FID) and Structural Similarity Index Measure (SSIM), provide insights into the trade-offs between perceptual quality and structural preservation. For paired image translation, pix2pix achieves the best FID scores due to its paired supervision and PatchGAN discriminator, while the autoencoder model attains the highest SSIM, suggesting better structural fidelity despite producing blurrier outputs. Among unpaired methods, CycleGAN achieves a competitive FID score by leveraging cycle-consistency loss, whereas CUT, which replaces cycle-consistency with contrastive learning, attains higher SSIM, indicating improved spatial similarity retention. Notably, incorporating depth information into CUT results in the lowest overall FID score, demonstrating that depth cues enhance realism. However, the slight decrease in SSIM suggests that depth-aware learning may introduce structural variations.