Generating high-quality sketches from video requires a nuanced understanding of semantic content and visual structure, particularly for complex scenes across diverse sketch styles. Efficient and flexible video-to-sketch style transformation remains a significant challenge. We introduce VideoSketcher, a training-free framework for style-controllable sketch video generation that preserves frame structure while applying specified sketch aesthetics. Leveraging text-to-image diffusion models, VideoSketcher utilizes strong semantic priors without the need for extensive training. Our approach enforces temporal consistency by retaining latent information across frames and employs a Time-Linked Attention mechanism to capture structural elements from the source video and inject stylistic information from the reference image. To bridge the semantic gap between sketches and original video content, we introduce Sketch Directive Amplification for selective transfer of stylistic features. Additionally, a Stroke Graph Regularization strategy, comprising line and point loss, refines line consistency in the latent space. Extensive experiments validate VideoSketcher’s superior temporal stability and fidelity across diverse sketch styles and content. Video demos can be found in the supplementary materials.

Automatic 3D shape analysis is heavily influenced by the pose of input 3D models, as the continuous nature of pose space introduces complexities that usually exceed the encoding capacities of standard deep learning frameworks. To tackle this challenge, we present Upright-Net+, an enhancement of our previous model, Upright-Net, specifically developed for estimating upright orientation in 3D point clouds. Our approach is grounded in the design principle that "form ever follows function," treating the natural base of an object as a functional structure that stabilizes it in its typical pose, influenced by physical laws and geometric properties. We reformulate the continuous orientation problem into a discrete classification task, focusing on learning the points that constitute the natural base of a 3D model. The upright orientation is determined by aligning the normal orientation of this base towards the mass center. To mitigate over-smoothing in the global feature embeddings from stacked graph convolutional layers, we introduce a Global Positional Encoding Module using Relative Distance Histogram Statistics Embedding (GPE-RDHS), which reduces structural ambiguity and enhances orientation estimation. We also enhanced a weighted residual loss term to penalize false positive predictions, enhancing overall model performance. Our method demonstrates exceptional performance in upright orientation estimation and reveals that the learned orientation-aware features significantly benefit downstream tasks, particularly in classification.

A mass of experiments shows that the pose of the input 3D models exerts a tremendous influence on automatic 3D shape analysis. In this paper, we propose Upright-Net, a deep-learning-based approach for estimating the upright orientation of 3D point clouds. Based on a well-known postulate of design states that “form ever follows function”, we treat the natural base of an object as a common functional structure, which supports the object in a most commonly seen pose following a set of specific rules, e.g. physical laws, functionality-related geometric properties, semantic cues, and so on. Thus we apply a data-driven deep learning method to automatically encode those rules and formulate the upright orientation estimation problem as a classification model, i.e. extract the points on a 3D model that forms the natural base. And then the upright orientation is computed as the normal of the natural base. Our proposed new approach has three advantages. First, it formulates the continuous orientation estimation task as a discrete classification task while preserving the continuity of the solution space. Second, it automatically learns the comprehensive criteria defining a natural base of general 3D models even with asymmetric geometry. Third, the learned orientation-aware features can serve well in downstream tasks. Results show that our network outperforms previous approaches on orientation estimation and also achieves remarkable generalization capability and transfer capability.

We propose a method for animating still manga imagery through camera movements. Given a series of existing manga pages, we start by automatically extracting panels, comic characters, and balloons from the manga pages. Then, we use a data-driven graphical model to infer per-panel motion and emotion states from low-level visual patterns. Finally, by combining domain knowledge of film production and characteristics of manga, we simulate camera movements over the manga pages, yielding an animation. The results augment the still manga contents with animated motion that reveals the mood and tension of the story, while maintaining the original narrative. We have tested our method on manga series of different genres, and demonstrated that our method can generate animations that are more effective in storytelling and pacing, with less human efforts, as compared with prior works. We also show two applications of our method, mobile comic reading, and comic trailer generation.

We present a novel approach that allows web designers to easily direct user attention via visual flow on web designs. By collecting and analyzing users' eye gaze data on real-world webpages under the task-driven condition, we build two user attention models that characterize user attention patterns between a pair of page components. These models enable a novel web design interaction for designers to easily create a visual flow to guide users' eyes (i.e., direct user attention along a given path) through a web design with minimal effort. In particular, given an existing web design as well as a designer-specified path over a subset of page components, our approach automatically optimizes the web design so that the resulting design can direct users' attention to move along the input path. We have tested our approach on various web designs of different categories. Results show that our approach can effectively guide user attention through the web design according to the designer's high-level specification.

Automatically extracting frames/panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas. However, automatic panel extraction for manga, i.e., Japanese comics, can be especially challenging, largely because of its complex panel layout design mixed with various visual symbols throughout the page. In this paper, we propose a robust method for automatically extracting panels from digital manga pages. Our method first extracts the panel block by closing open panels and identifying a page background mask. It then performs a recursive binary splitting to partition the panel block into a set of sub-blocks, where an optimal splitting line at each recursive level is determined adaptively.