This document contains additional details and experiments that did not fit in the main text due to space constraints. For animations and additional results please also check the included videos. We use a similar optimization strategy with DreamFusion, so unless otherwise noted the hyperparameters remain the same. For example, we use the Distributed Shampoo optimizer [2]. Similarly with DreamFusion we also train on a TPUv4 machine with 4 chips.

We present DreamHuman, a method to generate realistic animatable 3D human avatar models solely from textual descriptions.

We present \emph{DreamHuman}, a method to generate realistic animatable 3D human avatar models entirely from textual descriptions. Recent text-to-3D methods have made considerable strides in generation, but are still lacking in important aspects. Control and often spatial resolution remain limited, existing methods produce fixed rather than 3D human models that can be placed in different poses (i.e.

We present \emph{DreamHuman}, a method to generate realistic animatable 3D human avatar models entirely from textual descriptions. Recent text-to-3D methods have made considerable strides in generation, but are still lacking in important aspects. Control and often spatial resolution remain limited, existing methods produce fixed rather than 3D human models that can be placed in different poses (i.e. This makes it possible to generate dynamic 3D human avatars with high-quality textures and learnt per-instance rigid and non rigid geometric deformations. We demonstrate that our method is capable to generate a wide variety of animatable, realistic 3D human models from text.