Mira Dontcheva

Xingyu Bruce Liu, Mira Dontcheva, Dingzeyu Li

Everyone can write their stories in freeform text format -- it's something we all learn in school. Yet storytelling via video requires one to learn specialized and complicated tools. In this paper, we introduce Doki, a text-native interface for generative video authoring, aligning video creation with the natural process of text writing. In Doki, writing text is the primary interaction: within a single document, users define assets, structure scenes, create shots, refine edits, and add audio. We articulate the design principles of this text-first approach and demonstrate Doki's capabilities through a series of examples. To evaluate its real-world use, we conducted a week-long deployment study with participants of varying expertise in video authoring. This work contributes a fundamental shift in generative video interfaces, demonstrating a powerful and accessible new way to craft visual stories.

Catherine Yeh, Anh Truong, Mira Dontcheva et al.

Video storytelling is often constrained by available material, limiting creative expression and leaving undesired narrative gaps. Generative video offers a new way to address these limitations by augmenting captured media with tailored visuals. To explore this potential, we interviewed eight video creators to identify opportunities and challenges in integrating generative video into their workflows. Building on these insights and established filmmaking principles, we developed Vidmento, a tool for authoring hybrid video stories that combine captured and generated media through context-aware expansion. Vidmento surfaces opportunities for story development, generates clips that blend stylistically and narratively with surrounding media, and provides controls for refinement. In a study with 12 creators, Vidmento supported narrative development and exploration by systematically expanding initial materials with generative media, enabling expressive video storytelling aligned with creative intent. We highlight how creators bridge story gaps with generative content and where they find this blending capability most valuable.

James W. Hennessey, Han Liu, Holger Winnemöller et al.

Accurately drawing 3D objects is difficult for untrained individuals, as it requires an understanding of perspective and its effects on geometry and proportions. Step-by-step tutorials break the complex task of sketching an entire object down into easy-to-follow steps that even a novice can follow. However, creating such tutorials requires expert knowledge and is a time-consuming task. As a result, the availability of tutorials for a given object or viewpoint is limited. How2Sketch addresses this problem by automatically generating easy-to-follow tutorials for arbitrary 3D objects. Given a segmented 3D model and a camera viewpoint,it computes a sequence of steps for constructing a drawing scaffold comprised of geometric primitives, which helps the user draw the final contours in correct perspective and proportion. To make the drawing scaffold easy to construct, the algorithm solves for an ordering among the scaffolding primitives and explicitly makes small geometric modifications to the size and location of the object parts to simplify relative positioning. Technically, we formulate this scaffold construction as a single selection problem that simultaneously solves for the ordering and geometric changes of the primitives. We demonstrate our algorithm for generating tutorials on a variety of man-made objects and evaluate how easily the tutorials can be followed with a user study.

Babak Saleh, Mira Dontcheva, Aaron Hertzmann et al.

Infographics are complex graphic designs integrating text, images, charts and sketches. Despite the increasing popularity of infographics and the rapid growth of online design portfolios, little research investigates how we can take advantage of these design resources. In this paper we present a method for measuring the style similarity between infographics. Based on human perception data collected from crowdsourced experiments, we use computer vision and machine learning algorithms to learn a style similarity metric for infographic designs. We evaluate different visual features and learning algorithms and find that a combination of color histograms and Histograms-of-Gradients (HoG) features is most effective in characterizing the style of infographics. We demonstrate our similarity metric on a preliminary image retrieval test.