LINR Bridge: Vector Graphic Animation via Neural Implicits and Video Diffusion Priors

Vector graphics, known for their scalability and user-friendliness, provide a unique approach to visual content compared to traditional pixel-based images. Animation of these graphics, driven by the motion of their elements, offers enhanced comprehensibility and controllability but often requires substantial manual effort. To automate this process, we propose a novel method that integrates implicit neural representations with text-to-video diffusion models for vector graphic animation. Our approach employs layered implicit neural representations to reconstruct vector graphics, preserving their inherent properties such as infinite resolution and precise color and shape constraints, which effectively bridges the large domain gap between vector graphics and diffusion models. The neural representations are then optimized using video score distillation sampling, which leverages motion priors from pretrained text-to-video diffusion models. Finally, the vector graphics are warped to match the representations resulting in smooth animation. Experimental results validate the effectiveness of our method in generating vivid and natural vector graphic animations, demonstrating significant improvement over existing techniques that suffer from limitations in flexibility and animation quality.

ANYPORTAL: Zero-Shot Consistent Video Background Replacement

Despite the rapid advancements in video generation technology, creating high-quality videos that precisely align with user intentions remains a significant challenge. Existing methods often fail to achieve fine-grained control over video details, limiting their practical applicability. We introduce ANYPORTAL, a novel zero-shot framework for video background replacement that leverages pre-trained diffusion models. Our framework collaboratively integrates the temporal prior of video diffusion models with the relighting capabilities of image diffusion models in a zero-shot setting. To address the critical challenge of foreground consistency, we propose a Refinement Projection Algorithm, which enables pixel-level detail manipulation to ensure precise foreground preservation. ANYPORTAL is training-free and overcomes the challenges of achieving foreground consistency and temporally coherent relighting. Experimental results demonstrate that ANYPORTAL achieves high-quality results on consumer-grade GPUs, offering a practical and efficient solution for video content creation and editing.

Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation

Artistic text generation aims to amplify the aesthetic qualities of text while maintaining readability. It can make the text more attractive and better convey its expression, thus enjoying a wide range of application scenarios such as social media display, consumer electronics, fashion, and graphic design. Artistic text generation includes artistic text stylization and semantic typography. Artistic text stylization concentrates on the text effect overlaid upon the text, such as shadows, outlines, colors, glows, and textures. By comparison, semantic typography focuses on the deformation of the characters to strengthen their visual representation by mimicking the semantic understanding within the text. This overview paper provides an introduction to both artistic text stylization and semantic typography, including the taxonomy, the key ideas of representative methods, and the applications in static and dynamic artistic text generation. Furthermore, the dataset and evaluation metrics are introduced, and the future directions of artistic text generation are discussed. A comprehensive list of artistic text generation models studied in this review is available at https://github.com/williamyang1991/Awesome-Artistic-Typography/.