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

Domain generalization faces challenges due to the distribution shift between
training and testing sets, and the presence of unseen target domains. Common
solutions include domain alignment, meta-learning, data augmentation, or
ensemble learning, all of which rely on domain labels or domain adversarial
techniques. In this paper, we propose a Dual-Stream Separation and
Reconstruction Network, dubbed DSDRNet. It is a disentanglement-reconstruction
approach that integrates features of both inter-instance and intra-instance
through dual-stream fusion. The method introduces novel supervised signals by
combining inter-instance semantic distance and intra-instance similarity.
Incorporating Adaptive Instance Normalization (AdaIN) into a two-stage cyclic
reconstruction process enhances self-disentangled reconstruction signals to
facilitate model convergence. Extensive experiments on four benchmark datasets
demonstrate that DSDRNet outperforms other popular methods in terms of domain
generalization capabilities.

我们提出了一种名为 DSDRNet 的双流分离与重建网络，它是一种通过双流融合将 inter-instance 和 intra-instance 的特征相结合的解缠结重建方法。该方法通过结合 inter-instance 语义距离和 intra-instance 相似性引入了新颖的监督信号。将自适应实例归一化（AdaIN）引入到两阶段循环重建过程中，增强了自我解缠结重建信号以促进模型收敛。对四个基准数据集进行的大量实验证明，DSDRNet 在域泛化能力方面优于其他热门方法。