Mengwei Ren

I am a Research Scientist at Adobe. I obtained my Ph.D. in Computer Science from NYU (2023), supervised by Prof. Guido Gerig under Visualization and Data Analytics Research (VIDA) Center . I have been fortunate to intern at Adobe, Google Research, and Siemens Healthineers.

My research broadly lies at the intersection of computer vision , deep learning, and biomedical image analysis. Particularly, I am interested in generative models, representation learning and spatiotemporal analysis.

I am looking for a research intern for the summer of 2025 (current PhD students in US), working on image composition and generative models. Please feel free to email your resume and background to my email .

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We introduce Relightful Harmonization, a lighting-aware diffusion model designed to seamlessly harmonize sophisticated lighting effect for the foreground portrait using any background image.

CNN-extracted global and local features are limited in capturing long-range spatial dependencies that are essential in biological anatomy. We present a keypoint-augmented fusion layer that extracts representations preserving both short- and long-range self-attention in a self-supervised manner.

Image-conditioned Diffusion Probablistic Models (icDPMs) for restoration work well on benchmarks but not real images. We introduce a simple yet effective structure guidance that leads to significantly better visual quality on unseen images.

We propose a local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal (non i.i.d) images.

Based on an underlying assumption that morphological shape is consistent across imaging sites, we propose a segmentation-renormalized image translation framework to reduce inter-scanner heterogeneity while preserving anatomical layout.

We propose a generative adversarial translation framework for high-quality DW image estimation with arbitrary Q-space sampling given commonly acquired structural images.

We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework conditioned on flexible image covariates.

cr: Jon Baron