In this work, we propose a novel pipeline that combines AdaIN and NeRF for
the task of stylized Novel View Synthesis. Compared to previous works, we make
the following contributions: 1) We simplify the pipeline. 2) We extend the
capabilities of model to handle the multi-style task. 3) We modify the model
architecture to perform well on styles with strong brush strokes. 4) We
implement style interpolation on the multi-style model, allowing us to control
the style between any two styles and the style intensity between the stylized
output and the original scene, providing better control over the stylization
strength.

我们提出了一种结合 AdaIN 和 NeRF 的新型流程，用于风格化的新视角合成任务，相比之前的工作，我们做出了以下贡献：1）我们简化了流程。2）我们扩展了模型的能力，以处理多风格任务。3）我们修改了模型架构，以在具有强烈笔触的风格上表现出色。4）我们在多风格模型上实现了风格插值，使我们能够控制任意两种风格之间的风格和风格强度，从而更好地控制风格化的强度。

多风格自适应正规域的神经光辐射

Multi-style Neural Radiance Field with AdaIN

Cross-domain mapping has been a very active topic in recent years. Given one
image, its main purpose is to translate it to the desired target domain, or
multiple domains in the case of multiple labels. This problem is highly
challenging due to three main reasons: (i) unpaired datasets, (ii) multiple
attributes, and (iii) the multimodality (e.g., style) associated with the
translation. Most of the existing state-of-the-art has focused only on two
reasons, i.e. either on (i) and (ii), or (i) and (iii). In this work, we
propose a joint framework (i, ii, iii) of diversity and multi-mapping
image-to-image translations, using a single generator to conditionally produce
countless and unique fake images that hold the underlying characteristics of
the source image. Our system does not use style regularization, instead, it
uses an embedding representation that we call domain embedding for both domain
and style. Extensive experiments over different datasets demonstrate the
effectiveness of our proposed approach in comparison with the state-of-the-art
in both multi-label and multimodal problems. Additionally, our method is able
to generalize under different scenarios: continuous style interpolation,
continuous label interpolation, and fine-grained mapping. Code and pretrained
models are available at this https URL

本文提出了一种联合框架，通过条件生成无数独特的图像，实现多样性和多映射图像到图像的翻译，使用称为领域嵌入的嵌入表示来表示领域和风格，并在连续的样式插值，连续标签插值和细粒度映射等不同场景下展示其有效性。