PASDiff: Physics-Aware Semantic Guidance for Joint Real-World Low-Light Face Enhancement and Restoration

2026-03-26 · Yilin Ni, Wenjie Li, Zhengxue Wang, Juncheng Li, Guangwei Gao, Jian Yang arxiv

Face images captured in real-world low light suffer multiple degradations-low illumination, blur, noise, and low visibility, etc. Existing cascaded solutions often suffer from severe error accumulation, while generic joint models lack explicit facial priors and struggle to resolve clear face structures. In this paper, we propose PASDiff, a Physics-Aware Semantic Diffusion with a training-free manner. To achieve a plausible illumination and color distribution, we leverage inverse intensity weighting and Retinex theory to introduce photometric constraints, thereby reliably recovering visibility and natural chromaticity. To faithfully reconstruct facial details, our Style-Agnostic Structural Injection (SASI) extracts structures from an off-the-shelf facial prior while filtering out its intrinsic photometric biases, seamlessly harmonizing identity features with physical constraints. Furthermore, we construct WildDark-Face, a real-world benchmark of 700 low-light facial images with complex degradations. Extensive experiments demonstrate that PASDiff significantly outperforms existing methods, achieving a superior balance among natural illumination, color recovery, and identity consistency. Code and dataset will be available at https://github.com/IVIPLab/PASDiff.

📄 PDF Abstract BibTeX arXiv:2603.24969

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PISE: Physics-Anchored Semantically-Enhanced Deep Computational Ghost Imaging for Robust Low-Bandwidth Machine Perception

2026-01-18 · Tong Wu arxiv

We propose PISE, a physics-informed deep ghost imaging framework for low-bandwidth edge perception. By combining adjoint operator initialization with semantic guidance, PISE improves classification accuracy by 2.57% and …

Decoupled Diffusion Sampling for Inverse Problems on Function Spaces

2026-01-30 · Thomas Y. L. Lin, Jiachen Yao, Lufang Chiang, Julius Berner 외 arxiv

We propose a data-efficient, physics-aware generative framework in function space for inverse PDE problems. Existing plug-and-play diffusion posterior samplers represent physics implicitly through joint coefficient-solut…

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2026-03-05 · Weikai Qin, Sichen Wu, Ci Chen, Mengfan Liu 외 arxiv

In the domain of humanoid robot control, the fusion of Vision-Language-Action (VLA) with whole-body control is essential for semantically guided execution of real-world tasks. However, existing methods encounter challeng…

PhysVideoGenerator: Towards Physically Aware Video Generation via Latent Physics Guidance

2026-01-07 · Siddarth Nilol Kundur Satish, Devesh Jaiswal, Hongyu Chen, Abhishek Bakshi arxiv

Current video generation models produce high-quality aesthetic videos but often struggle to learn representations of real-world physics dynamics, resulting in artifacts such as unnatural object collisions, inconsistent g…

Video Generation

Joint 3D Gravity and Magnetic Inversion via Rectified Flow and Ginzburg-Landau Guidance

2026-03-06 · Dhruman Gupta, Yashas Shende, Aritra Das, Chanda Grover Kamra 외 arxiv

Subsurface ore detection is of paramount importance given the rising depletion of shallow mineral resources in recent years. It is crucial to explore approaches that go beyond the limitations of traditional geological ex…