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Neural Video Compression with Spatio-Temporal Cross-Covariance Transformers

Neural Video Compression with Spatio-Temporal Cross-Covariance Transformers

by America Ortiz | Oct 28, 2023 | Video Processing, Visual Computing

Neural Video Compression with Spatio-Temporal Cross-Covariance Transformers This work aims to effectively and jointly leverage robust temporal and spatial information by proposing a new 3D-based transformer module: Spatio-Temporal Cross- Covariance Transformer...
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...
Controllable Inversion of Black-Box Face Recognition Models via Diffusion

Controllable Inversion of Black-Box Face Recognition Models via Diffusion

by America Ortiz | Oct 2, 2023 | Machine Learning

Controllable Inversion of Black-Box Face Recognition Models via Diffusion We tackle the challenging task of inverting the latent space of pre-trained face recognition models without full model access (i.e. black-box setting). Our method, the identity denoising...
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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