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title
DRaCoN – Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
description
Avatar animation
image
site name
author
updated
2026-02-25 08:16:30
raw text
DRaCoN – Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars DRaCoN – Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars Amit Raj 1 Umar Iqbal 2 Koki Nagano 2 Sameh Khamis 2 Pavlo Molchanov 2 James Hays 1 Jan Kautz 2 1 Georgia Institute of Technology 2 NVIDIA Research [Paper] Abstract Acquisition and creation of digital human avatars is an important problem with applications to virtual telepresence, gaming, and human modeling. Most contemporary approaches for avatar generation can be viewed either as 3D-based methods, which use multi-view data to learn a 3D representation with appearance (such as a mesh, implicit surface, or volume), or 2D-based methods which learn photo-realistic renderings of avatars but lack accurate 3D representations. In this work, we present, DRaCoN, a framework for learning full-body volumetric avatars which exploits the advantages of both the 2D ...
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