SiPhy: Single-Image Physical Property Reasoning

Inferring physical properties such as mass, stiffness, and elasticity from a single image is essential for simulation and embodied AI, yet most existing approaches rely on multi-view reconstruction or physics-based supervision. We introduce SiPhy, a unified framework for single-image physical property reasoning that aligns 3D-aware visual cues, depth with language-based material knowledge. From one RGB image, SiPhy samples pseudo-voxel points, extracts CLIP features, and grounds them to material candidates proposed by a VLM. A part-based contrastive aggregator enforces region consistency, while a heaviness-aware refinement improves thickness and volume estimation for dense objects. Across ABO-500, MVImgNet-100, and PhysXNet-100, SiPhy achieves state-of-the-art single-image performance, surpassing multi-view reconstruction methods by improving mass MnRE by up to 93% (vs. PUGS), reducing density MAE by 35.5% (vs. NeRF2Physics), and lowering Young's modulus error by 23.5%. We further validate SiPhy on real hand-object interaction datasets, demonstrating its potential as a data annotation engine for physical understanding from single-view imagery.

Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures

Spanning multiple scales-from macroscopic anatomy down to intricate microscopic architecture-the human brain exemplifies a complex system that demands integrated approaches to fully understand its complexity. Yet, mapping nonlinear relationships between these scales remains challenging due to technical limitations and the high cost of multimodal Magnetic Resonance Imaging (MRI) acquisition. Here, we introduce Macro2Micro, a deep learning framework that predicts brain microstructure from macrostructure using a Generative Adversarial Network (GAN). Grounded in the scale-free, self-similar nature of brain organization-where microscale information can be inferred from macroscale patterns-Macro2Micro explicitly encodes multiscale brain representations into distinct processing branches. To further enhance image fidelity and suppress artifacts, we propose a simple yet effective auxiliary discriminator and learning objective. Our results show that Macro2Micro faithfully translates T1-weighted MRIs into corresponding Fractional Anisotropy (FA) images, achieving a 6.8% improvement in the Structural Similarity Index Measure (SSIM) compared to previous methods, while preserving the individual neurobiological characteristics.

A Training-Free Approach for Music Style Transfer with Latent Diffusion Models

Music style transfer, while offering exciting possibilities for personalized music generation, often requires extensive training or detailed textual descriptions. This paper introduces a novel training-free approach leveraging pre-trained Latent Diffusion Models (LDMs). By manipulating the self-attention features of the LDM, we effectively transfer the style of reference music onto content music without additional training. Our method achieves superior style transfer and melody preservation compared to existing methods. This work opens new creative avenues for personalized music generation.

AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a lightweight but effective model, AesFA -- Aesthetic Feature-Aware NST. The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely. To improve the network's ability to extract more distinct representations and further enhance the stylization quality, this work introduces a new aesthetic feature: contrastive loss. Extensive experiments and ablations show the approach not only outperforms recent NST methods in terms of stylization quality, but it also achieves faster inference. Codes are available at https://github.com/Sooyyoungg/AesFA.