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Empowering Convolutional Neural Networks with MetaSin Activation

Empowering Convolutional Neural Networks with MetaSin Activation

by America Ortiz | Dec 9, 2023 | Video Processing, Visual Computing

Empowering Convolutional Neural Networks with MetaSin Activation In this work, we propose replacing a baseline network’s existing activations with a novel ensemble function with trainable parameters. The proposed METASIN activation can be trained reliably without...
An Implicit Physical Face Model Driven by Expression and Style

An Implicit Physical Face Model Driven by Expression and Style

by America Ortiz | Nov 29, 2023 | Capture, Visual Computing

An Implicit Physical Face Model Driven by Expression and Style We propose a new face model based on a data-driven implicit neural physics model that can be driven by both expression and style separately. At the core, we present a framework for learning implicit...
A Perceptual Shape Loss for Monocular 3D Face Reconstruction

A Perceptual Shape Loss for Monocular 3D Face Reconstruction

by America Ortiz | Oct 9, 2023 | Capture, Visual Computing

A Perceptual Shape Loss for Monocular 3D Face Reconstruction In this work, we propose a new loss function for monocular face capture, inspired by how humans would perceive the quality of a 3D face reconstruction given a particular image. It is widely known that...
ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting

ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting

by America Ortiz | Oct 2, 2023 | Capture, Visual Computing

ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting In this paper, we target the application scenario of capturing high-fidelity assets for neural relighting in controlled studio conditions, but without requiring a dense light stage. Instead, we...
The Score-Difference Flow for Implicit Generative Modeling

The Score-Difference Flow for Implicit Generative Modeling

by America Ortiz | Aug 31, 2023 | Machine Learning

The Score-Difference Flow for Implicit Generative Modeling We present the score difference (SD) between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them. July 2023 Transactions on Machine Learning...
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