3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtained impressive results in stylizing 3D scenes. However, the mod- els proposed by these methods need to be re-trained when applied to a new scene. In other words, their models are cou- pled with a specific scene and cannot adapt to arbitrary other scenes. To address this issue, we propose a novel 3D scene stylization framework to transfer an arbitrary style to an ar- bitrary scene, without any style-related or scene-related re- training. Concretely, we first map the appearance of the 3D scene into a 2D style pattern space, which realizes complete disentanglement of the geometry and appearance of the 3D scene and makes our model be generalized to arbitrary 3D scenes. Then we stylize the appearance of the 3D scene in the 2D style pattern space via a prompt-based 2D stylization al- gorithm. Experimental results demonstrate that our proposed framework is superior to SOTA methods in both visual qual- ity and generalization.

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the network or designing complex noise injection functions, which is computationally expensive, task-specific, or struggle to generate high-quality results. Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters? To answer this question, we re-examine the conditional image generation tasks from the perspective of adversarial attack and propose a simple and efficient plug-in projected gradient descent (PGD) like method for diverse and controllable image generation. The key idea is attacking the pre-trained deterministic generative models by adding a micro perturbation to the input condition. In this way, diverse results can be generated without any adjustment of network structures or fine-tuning of the pre-trained models. In addition, we can also control the diverse results to be generated by specifying the attack direction according to a reference text or image. Our work opens the door to applying adversarial attack to low-level vision tasks, and experiments on various conditional image generation tasks demonstrate the effectiveness and superiority of the proposed method.

Recently proposed LaMa [25] introduce Fast Fourier Convolution (FFC) [4] into image inpainting. FFC empowers the fully convolutional network to have a global receptive field in its early layers, and have the ability to produce robust repeating texture. However, LaMa has difficulty in generating clear and sharp complex content. In this paper, we analyze the fundamental flaws of using FFC in image inpainting, which are 1) spectrum shifting, 2) unexpected spatial activation, and 3) limited frequency receptive field. Such flaws make FFC-based inpainting framework difficult in generating complicated texture and performing faithful reconstruction. Based on the above analysis, we propose a novel Unbiased Fast Fourier Convolution (UFFC) module. UFFC is constructed by modifying the vanilla FFC module with 1) range transform and inverse transform, 2) absolute position embedding, 3) dynamic skip connection, and 4) adaptive clip, to overcome the above flaws. UFFC captures frequency information efficiently and realize reconstruction without introducing additional artifacts, achieving better inpainting results and more efficient training. In addition, we propose two novel perceptual losses for better generation quality and more robust training. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our method, outperforming the state-of-the-art methods in both texture-capturing ability and expressiveness.

Text-driven 3D style transfer aims at stylizing a scene according to the text and generating arbitrary novel views with consistency. Simply combining image/video style transfer methods and novel view synthesis methods results in flickering when changing viewpoints, while existing 3D style transfer methods learn styles from images instead of texts. To address this problem, we for the first time design an efficient text-driven model for 3D style transfer, named TeSTNeRF, to stylize the scene using texts via cross-modal learning: we leverage an advanced text encoder to embed the texts in order to control 3D style transfer and align the input text and output stylized images in latent space. Furthermore, to obtain better visual results, we introduce style supervision, learning feature statistics from style images and utilizing 2D stylization results to rectify abrupt color spill. Extensive experiments demonstrate that TeSTNeRF significantly outperforms existing methods and provides a new way to guide 3D style transfer.