We present DreamHuman, a method to generate realistic animatable 3D human
avatar models solely 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 animated 3D human models, and anthropometric
consistency for complex structures like people remains a challenge. DreamHuman
connects large text-to-image synthesis models, neural radiance fields, and
statistical human body models in a novel modeling and optimization framework.
This makes it possible to generate dynamic 3D human avatars with high-quality
textures and learned, instance-specific, surface deformations. We demonstrate
that our method is capable to generate a wide variety of animatable, realistic
3D human models from text. Our 3D models have diverse appearance, clothing,
skin tones and body shapes, and significantly outperform both generic
text-to-3D approaches and previous text-based 3D avatar generators in visual
fidelity. For more results and animations please check our website at
this https URL

DreamHuman 是一种从文本描述自动生成逼真可动的三维人物头像模型的方法，该方法将大规模的文本到图像合成模型、神经辐射场和统计人体模型连接在一起，以新颖的建模和优化框架产生动态三维人物头像，具有高质量的纹理、实例特定的表面变形和多样化的外观、衣着、肤色和身体形态。