We present DreamWaltz, a novel framework for generating and animating complex
avatars given text guidance and parametric human body prior. While recent
methods have shown encouraging results in the text-to-3D generation of common
objects, creating high-quality and animatable 3D avatars remains challenging.
To create high-quality 3D avatars, DreamWaltz proposes 3D-consistent
occlusion-aware Score Distillation Sampling (SDS) to optimize implicit neural
representations with canonical poses. It provides view-aligned supervision via
3D-aware skeleton conditioning and enables complex avatar generation without
artifacts and multiple faces. For animation, our method learns an animatable
and generalizable avatar representation which could map arbitrary poses to the
canonical pose representation. Extensive evaluations demonstrate that
DreamWaltz is an effective and robust approach for creating 3D avatars that can
take on complex shapes and appearances as well as novel poses for animation.
The proposed framework further enables the creation of complex scenes with
diverse compositions, including avatar-avatar, avatar-object and avatar-scene
interactions.

DreamWaltz 是一个新型的框架，用于基于文本指导和参数人体先验生成和创建复杂的 3D 化身，该方法使用 3D 一致、遮挡感知的分数蒸馏采样优化隐式神经表示，并通过 3D 感知骨骼调节提供视角对齐的监督，其使复杂的化身生成更加完美且没有伪影和多个面，同时，该方法学习了一个可动画的、通用的化身表征，能够将任意的姿态映射到规范的姿态表示，其能有效地创建可以承担复杂形状、外观和新颖动画姿态的 3D 化身，使其能够与不同构场景进行交互。