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.

Sound effects (SFX) are critical to video storytelling by immersing viewers, directing attention, and shaping emotion. However, crafting an effective soundscape is difficult: creators must decide how to source, place, layer, and mix sounds to support the narrative. Generative text-to-SFX tools enable users to create custom sounds, but creators often struggle to describe sounds with words and lack control over individual stems in premixed outputs. We propose SoundStager, an AI-assisted tool for designing generative soundscapes for video. SoundStager analyzes the video narrative to create layered audio scenes (of keynote, signal, soundmark, and archetypal sounds) and supports iterative refinement through a combination of conversational and analog controls. SoundStager’s design was informed by formative studies with six professional sound designers, six video creators, and insights from sound design literature. Our user evaluation with twelve video creators shows that SoundStager enables users to quickly create satisfactory soundscapes while retaining creative control.

Video is a powerful medium for communication and storytelling, yet reauthoring existing footage remains challenging. Even simple edits often demand expertise, time, and careful planning, constraining how creators envision and shape their narratives. Recent advances in generative AI suggest a new paradigm: what if editing a video were as straightforward as rewriting text? To investigate this, we present a tech probe and a study on text-driven video reauthoring. Our approach involves two technical contributions: (1) a generative reconstruction algorithm that reverse-engineers video into an editable text prompt, and (2) an interactive probe, Rewrite Kit, that allows creators to manipulate these prompts. A technical evaluation of the algorithm reveals a critical human-AI perceptual gap. A probe study with 12 creators surfaced novel use cases such as virtual reshooting, synthetic continuity, and aesthetic restyling. It also highlighted key tensions around coherence, control, and creative alignment in this new paradigm. Our work contributes empirical insights into the opportunities and challenges of text-driven video reauthoring, offering design implications for future co-creative video tools.

CHI ’25, Yokohama, Japan

Tutorial videos are a valuable resource for people looking to learn new tasks. People often learn these skills by viewing multiple tutorial videos to get an overall understanding of a task by looking at different approaches to achieve the task. However, navigating through multiple videos can be time-consuming and mentally demanding as these videos are scattered and not easy to skim. We propose VideoMix, a system that helps users gain a holistic understanding of a how-to task by aggregating information from multiple videos on the task. Insights from our formative study (N=12) reveal that learners value understanding potential outcomes, required materials, alternative methods, and important details shared by different videos. Powered by a Vision-Language Model pipeline, VideoMix extracts and organizes this information, presenting concise textual summaries alongside relevant video clips, enabling users to quickly digest and navigate the content. A comparative user study (N=12) demonstrated that VideoMix enabled participants to gain a more comprehensive understanding of tasks with greater efficiency than a baseline video interface, where videos are viewed independently. Our findings highlight the potential of a task-oriented, multi-video approach where videos are organized around a shared goal, offering an enhanced alternative to conventional video-based learning.

Processing visual data often involves small adjustments or sequences of changes, e.g., image filtering, surface smoothing, and animation. While established graphics techniques like normal mapping and video compression exploit redundancy to encode such small changes efficiently, the problem of encoding small changes to neural fields—neural network parameterizations of visual or physical functions—has received less attention. We propose a parameter-efficient strategy for updating neural fields using low-rank adaptations (LoRA). LoRA, a method from the parameter-efficient fine-tuning LLM community, encodes small updates to pre-trained models with minimal computational overhead. We adapt LoRA for instance-specific neural fields, avoiding the need for large pre-trained models and yielding lightweight updates. We validate our approach with experiments in image filtering, geometry editing, video compression, and energy-based editing, demonstrating its effectiveness and versatility for representing neural field updates.

Video podcast teasers are short videos that can be shared on social media platforms to capture interest in full episodes of a video podcast. These teasers enable long-form podcasters to reach new audiences and gain more followers. However, creating a compelling teaser from an hour-long episode can be challenging. Selecting interesting clips requires significant mental effort; editing the chosen clips into a cohesive, well-produced teaser is time-consuming. To support the creation of video podcast teasers, we first investigated what makes a good teaser. We combined insights from audience comments and creator interviews to identify key ingredients. We also identified a common workflow used by creators during this process. Based on these findings, we developed a human-AI co-creative tool called PodReels to assist video podcasters in crafting teasers. Our user study demonstrated that PodReels significantly reduces creators’ mental demand and improves their efficiency in producing video podcast teasers.

Mixed-media tutorials, which integrate videos, images, text, and diagrams to teach procedural skills, offer more browsable alternatives than timeline-based videos. However, manually creating such tutorials is tedious, and existing automated solutions are often restricted to a particular domain. While AI models hold promise, it is unclear how to effectively harness their powers, given the multi-modal data involved and the vast landscape of models. We present TutoAI, a cross-domain framework for AI-assisted mixed-media tutorial creation on physical tasks. First, we distill common tutorial components by surveying existing work; then, we present an approach to identify, assemble, and evaluate AI models for component extraction; finally, we propose guidelines for designing user interfaces (UI) that support tutorial creation based on AI-generated components. We show that TutoAI has achieved higher or similar quality compared to a baseline model in preliminary user studies.

We present a multi-modal approach for automatically generating hierarchical tutorials from instructional makeup videos. Our approach is inspired by prior research in cognitive psychology, which suggests that people mentally segment procedural tasks into event hierarchies, where coarse-grained events focus on objects while fine-grained events focus on actions. In the instructional makeup domain, we find that objects correspond to facial parts while fine-grained steps correspond to actions on those facial parts. Given an input instructional makeup video, we apply a set of heuristics that combine computer vision techniques with transcript text analysis to automatically identify the fine-level action steps and group these steps by facial part to form the coarse-level events. We provide a voice-enabled, mixed-media UI to visualize the resulting hierarchy and allow users to efficiently navigate the tutorial (e.g., skip ahead, return to previous steps) at their own pace. Users can navigate the hierarchy at both the facial-part and action-step levels using click-based interactions and voice commands. We demonstrate the effectiveness of segmentation algorithms and the resulting mixed-media UI on a variety of input makeup videos. A user study shows that users prefer following instructional makeup videos in our mixed-media format to the standard video UI and that they find our format much easier to navigate.

When watching how-to videos related to physical tasks, users' hands are often occupied by the task, making voice input a natural fit. To better understand the design space of voice interactions for how-to video navigation, we conducted three think-aloud studies using: 1) a traditional video interface, 2) a research probe providing a voice controlled video interface, and 3) a wizard-of-oz interface. From the studies, we distill seven navigation objectives and their underlying intents: pace control pause, content alignment pause, video control pause, reference jump, replay jump, skip jump, and peek jump. Our analysis found that users' navigation objectives and intents affect the choice of referent type and referencing approach in command utterances. Based on our findings, we recommend to 1) support conversational strategies like sequence expansions and command queues, 2) allow users to identify and refine their navigation objectives explicitly, and 3) support the seven interaction intents.

There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases.

Video summaries are a popular way to share important events, but creating good summaries is hard. It requires expertise in both capturing and editing footage. While hiring a professional videographer is possible, this is too costly for most casual events. An alternative is to place 360 video cameras around an event space to capture footage passively and then extract regular field-of-view (RFOV) shots for the summary. This paper focuses on the problem of extracting such RFOV shots. Since we cannot actively control the cameras or the scene, it is hard to create "ideal' shots that adhere strictly to traditional cinematography rules. To better understand the tradeoffs, we study human preferences for static and moving camera RFOV shots generated from 360 footage. From the findings, we derive design guidelines. As a secondary contribution, we use these guidelines to develop automatic algorithms that we demonstrate in a prototype user interface for extracting RFOV shots from 360 videos.

Motivated by many real-world examples, such as recommendation systems or sensor fusion, and aiming to capture the influence of malicious experts who intentionally degrade the performance of learning systems, we analyze optimal adversarial strategies against the weighted average prediction algorithm in the learning with expert advice framework. All but one expert is honest and the malicious expert's goal is to sabotage the performance of the algorithm by strategically providing dishonest recommendations. We formulate the problem as a Markov decision process and analyze it under various settings. For the logarithmic loss, somewhat surprisingly, we prove that the optimal strategy for the adversary is the greedy policy, i.e., lying at every step. For the absolute loss, in the 2-experts, discounted cost setting, we prove that the optimal strategy is a threshold policy, where the malicious expert tells the truth until he earns enough weight and then lies afterwards. We extend the results to the infinite horizon problem and find the exact thresholds for the stationary optimal policy. Finally, we use a mean field approach in the N-experts setting to find the optimal strategy when the predictions of the honest experts are independent and identically distributed. We justify our results using simulations throughout this paper.

We consider a generalized model of learning from expert advice in which experts could abstain from participating at some rounds. Our proposed online algorithm falls into the class of weighted average predictors and uses a time-varying multiplicative weight update rule. This update rule changes the weight of an expert based on his or her relative performance compared to the average performance of available experts at the current round. This makes the algorithm suitable for recommendation systems in the presence of an adversary with many potential applications in the new emerging area of the Internet of Things. We prove the convergence of our algorithm to the best expert, defined in terms of both availability and accuracy, in the stochastic setting. In particular, we show the applicability of our definition of best expert through convergence analysis of another well-known algorithm in this setting. Finally, through simulation results on synthetic and real datasets, we justify the out-performance of our proposed algorithms compared to the existing ones in the literature.

Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models. We present an automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a high-fidelity 3D model. Our method uses a coarse estimate of the scene geometry to plan camera trajectories that: (1) cover the scene as thoroughly as possible; (2) encourage observations of scene geometry from a diverse set of viewing angles; (3) avoid obstacles; and (4) respect a user-specified flight time budget. Our method relies on a mathematical model of scene coverage that exhibits an intuitive diminishing returns property known as submodularity. We leverage this property extensively to design a trajectory planning algorithm that reasons globally about the non-additive coverage reward obtained across a trajectory, jointly with the cost of traveling between views. We evaluate our method by using it to scan three large outdoor scenes, and we perform a quantitative evaluation using a photorealistic video game simulator.

We present a system for efficiently editing video of dialogue-driven scenes. The input to our system is a standard film script and multiple video takes, each capturing a different camera framing or performance of the complete scene. Our system then automatically selects the most appropriate clip from one of the input takes, for each line of dialogue, based on a user-specified set of film-editing idioms. Our system starts by segmenting the input script into lines of dialogue and then splitting each input take into a sequence of clips time-aligned with each line. Next, it labels the script and the clips with high-level structural information (e.g., emotional sentiment of dialogue, camera framing of clip, etc.). After this pre-process, our interface offers a set of basic idioms that users can combine in a variety of ways to build custom editing styles. Our system encodes each basic idiom as a Hidden Markov Model that relates editing decisions to the labels extracted in the pre-process. For short scenes (< 2 minutes, 8--16 takes, 6--27 lines of dialogue) applying the user-specified combination of idioms to the pre-processed inputs generates an edited sequence in 2--3 seconds. We show that this is significantly faster than the hours of user time skilled editors typically require to produce such edits and that the quick feedback lets users iteratively explore the space of edit designs.

We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.

Cameras attached to small quadrotor aircraft are rapidly becoming a ubiquitous tool for cinematographers, enabling dynamic camera movements through 3D environments. Currently, professionals use these cameras by flying quadrotors manually, a process which requires much skill and dexterity. In this paper, we investigate the needs of quadrotor cinematographers, and build a tool to support video capture using quadrotor-based camera systems. We begin by conducting semi-structured interviews with professional photographers and videographers, from which we extract a set of design principles. We present a tool based on these principles for designing and autonomously executing quadrotor-based camera shots. Our tool enables users to: (1) specify shots visually using keyframes; (2) preview the resulting shots in a virtual environment; (3) precisely control the timing of shots using easing curves; and (4) capture the resulting shots in the real world with a single button click using commercially available quadrotors. We evaluate our tool in a user study with novice and expert cinematographers. We show that our tool makes it possible for novices and experts to design compelling and challenging shots, and capture them fully autonomously.