ANR: Articulated Neural Rendering for Virtual Avatars

TL;DR

ANR introduces a neural rendering framework that effectively handles articulated virtual avatars, improving realism, stability, and detail over existing methods through explicit deformation modeling.

Contribution

It extends Deferred Neural Rendering to better accommodate articulated objects like virtual avatars, addressing deformation and alignment challenges.

Findings

Users prefer ANR-generated avatars in studies.

ANR outperforms existing methods on quantitative metrics.

Enhanced temporal stability and detail in avatar rendering.

Abstract

The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the resulting images. Using skinned meshes for rendering articulating objects is a natural extension for the DNR framework and would open it up to a plethora of applications. However, in this case the neural shading step must account for deformations that are possibly not captured in the mesh, as well as alignment inaccuracies and dynamics -- which can confound the DNR pipeline. We present Articulated Neural Rendering (ANR), a novel framework based on DNR which explicitly addresses its limitations for virtual human avatars. We show the superiority of ANR not only with respect to DNR but also with methods specialized for avatar creation and animation. In two user studies, we observe a clear preference for our avatar model and we demonstrate state-of-the-art performance on quantitative evaluation metrics. Perceptually, we observe better temporal stability, level of detail and plausibility.