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Ange Lou

4 accepted papers

2026

Towards Generalizable EEG-to-fMRI Synthesis via a Unified, Context-Aware Prompting Framework

ICML 2026poster

Functional magnetic resonance imaging (fMRI) provides dynamic measurements of human brain activity at high spatial resolution and depth, but its use is constrained by high cost, limited accessibility, and strict acquisition requirements. Synthesizing fMRI data from more accessible, non-invasive moda…

Cited by 0SourceScholar
2024

DaReNeRF: Direction-aware Representation for Dynamic Scenes

CVPR 2024poster

Addressing the intricate challenge of modeling and re-rendering dynamic scenes most recent approaches have sought to simplify these complexities using plane-based explicit representations overcoming the slow training time issues associated with methods like Neural Radiance Fields (NeRF) and implicit…

Cited by 9SourcePDFScholar
2024

Divide and Fuse: Body Part Mesh Recovery from Partially Visible Human Images

ECCV 2024poster

"We introduce a novel bottom-up approach for human body mesh reconstruction, specifically designed to address the challenges posed by partial visibility and occlusion in input images. Traditional top-down methods, relying on whole-body parametric models like SMPL, falter when only a small part of th…

Cited by 2SourcePDFScholar
2024

NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping

NeurIPS 2024poster

Functional magnetic resonance imaging (fMRI) is an indispensable tool in modern neuroscience, providing a non-invasive window into whole-brain dynamics at millimeter-scale spatial resolution. However, fMRI is constrained by issues such as high operation costs and immobility. With the rapid advanceme…