1.3k citations · 1.8k across the 82 of their papers we have counts for
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RANA: Relightable Articulated Neural Avatars
Umar Iqbal, Akin Caliskan, Koki Nagano +3
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting. We only require a shor…
Global Context Vision Transformers
Ali Hatamizadeh, Hongxu Yin, Greg Heinrich +2
We propose global context vision transformer (GC ViT), a novel architecture that enhances parameter and compute utilization for computer vision. Our method leverages global context…
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
Amit Raj, Umar Iqbal, Koki Nagano +4
Acquisition and creation of digital human avatars is an important problem with applications to virtual telepresence, gaming, and human modeling. Most contemporary approaches for av…
GradViT: Gradient Inversion of Vision Transformers
Ali Hatamizadeh, Hongxu Yin, Holger Roth +4
In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given…
Do Gradient Inversion Attacks Make Federated Learning Unsafe?
Ali Hatamizadeh, Hongxu Yin, Pavlo Molchanov +8
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare application…