Text-to-3D is an emerging task that allows users to create 3D content with
infinite possibilities. Existing works tackle the problem by optimizing a 3D
representation with guidance from pre-trained diffusion models. An apparent
drawback is that they need to optimize from scratch for each prompt, which is
computationally expensive and often yields poor visual fidelity. In this paper,
we propose DreamPortrait, which aims to generate text-guided 3D-aware portraits
in a single-forward pass for efficiency. To achieve this, we extend Score
Distillation Sampling from datapoint to distribution formulation, which injects
semantic prior into a 3D distribution. However, the direct extension will lead
to the mode collapse problem since the objective only pursues semantic
alignment. Hence, we propose to optimize a distribution with hierarchical
condition adapters and GAN loss regularization. For better 3D modeling, we
further design a 3D-aware gated cross-attention mechanism to explicitly let the
model perceive the correspondence between the text and the 3D-aware space.
These elaborated designs enable our model to generate portraits with robust
multi-view semantic consistency, eliminating the need for optimization-based
methods. Extensive experiments demonstrate our model's highly competitive
performance and significant speed boost against existing methods.

本研究提出了一种名为 DreamPortrait 的算法，它可以在一个前向传递中生成文本引导下的 3D 感知画像，通过 Score Distillation Sampling 和 GAN 损失正则化方法来优化分布，进一步设计了 3D 感知门控交叉关注机制，生成高效且具有鲁棒的多视图语义一致性的人像。