Recent advancements in text-to-image generation have enabled significant
progress in zero-shot 3D shape generation. This is achieved by score
distillation, a methodology that uses pre-trained text-to-image diffusion
models to optimize the parameters of a 3D neural presentation, e.g. Neural
Radiance Field (NeRF). While showing promising results, existing methods are
often not able to preserve the geometry of complex shapes, such as human
bodies. To address this challenge, we present ZeroAvatar, a method that
introduces the explicit 3D human body prior to the optimization process.
Specifically, we first estimate and refine the parameters of a parametric human
body from a single image. Then during optimization, we use the posed parametric
body as additional geometry constraint to regularize the diffusion model as
well as the underlying density field. Lastly, we propose a UV-guided texture
regularization term to further guide the completion of texture on invisible
body parts. We show that ZeroAvatar significantly enhances the robustness and
3D consistency of optimization-based image-to-3D avatar generation,
outperforming existing zero-shot image-to-3D methods.

本研究提出了一种名为 ZeroAvatar 的方法，它使用人体先验约束来保持更复杂的图形（如人体）的几何形状以及完成不可见部位的纹理修复，并且在优化的图像到 3D 人体生成中表现出色，优于存在的一些零样本图像到 3D 方法。