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1. Animation Analytics in an Interactive Textbook for Material and Energy Balances

   Stone, S. J.; Crockett, B.; Xu, Kevin S.; Liberatore, M. W. (August 2022, ASEE Annual Conference proceedings)

   Interactive textbooks generate big data through student reading participation, including animations, question sets, and auto-graded homework. Animations are multi-step, dynamic visuals with text captions. By dividing new content into smaller chunks of information, student engagement is expected to be high, which aligns with tenets of cognitive load theory. Specifically, students’ clicks are recorded and measure usage, completion, and view time per step and for entire animations. Animation usage data from an interactive textbook for a chemical engineering course in Material and Energy Balances accounts for 60,000 animation views across 140+ unique animations. Data collected across five cohorts between 2016 and 2020 used various metrics to capture animation usage including watch and re-watch rates as well as the length of animation views. Variations in view rate and time were examined across content, parsed by book chapter, and five animation characterizations (Concept, Derivation, Figures and Plots, Physical World, and Spreadsheets). Important findings include: 1) Animation views were at or above 100% for all chapters and cohorts, 2) Median view time varies from 22 s (2-step) to 59 s (6-step) - a reasonable attention span for students and cognitive load, 3) Median view time for animations characterized as Derivation was the longest (40 s) compared to Physical World animations, which resulted in the shortest time (20 s). 

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2. MoVer: Motion Verification for Motion Graphics Animations

   https://doi.org/10.1145/3731209  

   Ma, Jiaju; Agrawala, Maneesh (August 2025, ACM Transactions on Graphics)

   While large vision-language models can generate motion graphics animations from text prompts, they regularly fail to include all spatio-temporal properties described in the prompt. We introduce MoVer, a motion verification DSL based on first-order logic that can check spatio-temporal properties of a motion graphics animation. We identify a general set of such properties that people commonly use to describe animations (e.g., the direction and timing of motions, the relative positioning of objects, etc.). We implement these properties as predicates in MoVer and provide an execution engine that can apply a MoVer program to any input SVG-based motion graphics animation. We then demonstrate how MoVer can be used in an LLM-based synthesis and verification pipeline for iteratively refining motion graphics animations. Given a text prompt, our pipeline synthesizes a motion graphics animation and a corresponding MoVer program. Executing the verification program on the animation yields a report of the predicates that failed and the report can be automatically fed back to LLM to iteratively correct the animation. To evaluate our pipeline, we build a synthetic dataset of 5600 text prompts paired with ground truth MoVer verification programs. We find that while our LLM-based pipeline is able to automatically generate a correct motion graphics animation for 58.8% of the test prompts without any iteration, this number raises to 93.6% with up to 50 correction iterations. Our code and dataset are at https://mover-dsl.github.io. 

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3. Gemini ² : Generating Keyframe-Oriented Animated Transitions Between Statistical Graphics

   https://doi.org/10.1109/VIS49827.2021.9623291  

   Kim, Younghoon; Heer, Jeffrey (October 2021, IEEE Visualization Conference (VIS))

   Complex animated transitions may be easier to understand when divided into separate, consecutive stages. However, effective staging requires careful attention to both animation semantics and timing parameters. We present Gemini^2, a system for creating staged animations from a sequence of chart keyframes. Given only a start state and an end state, Gemini^2 can automatically recommend intermediate keyframes for designers to consider. The Gemini^2 recommendation engine leverages Gemini, our prior work, and GraphScape to itemize the given complex change into semantic edit operations and to recombine operations into stages with a guided order for clearly conveying the semantics. To evaluate Gemini^2's recommendations, we conducted a human-subject study in which participants ranked recommended animations from both Gemini^2 and Gemini. We find that Gemini^2's animation recommendation ranking is well aligned with subjects' preferences, and Gemini^2 can recommend favorable animations that Gemini cannot support. 

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4. Disentangling audio content and emotion with adaptive instance normalization for expressive facial animation synthesis

   https://doi.org/10.1002/cav.2076  

   Chang, Che‐Jui; Zhao, Long; Zhang, Sen; Kapadia, Mubbasir (July 2022, Computer Animation and Virtual Worlds)

   Abstract 3D facial animation synthesis from audio has been a focus in recent years. However, most existing literature works are designed to map audio and visual content, providing limited knowledge regarding the relationship between emotion in audio and expressive facial animation. This work generates audio‐matching facial animations with the specified emotion label. In such a task, we argue that separating the content from audio is indispensable—the proposed model must learn to generate facial content from audio content while expressions from the specified emotion. We achieve it by an adaptive instance normalization module that isolates the content in the audio and combines the emotion embedding from the specified label. The joint content‐emotion embedding is then used to generate 3D facial vertices and texture maps. We compare our method with state‐of‐the‐art baselines, including the facial segmentation‐based and voice conversion‐based disentanglement approaches. We also conduct a user study to evaluate the performance of emotion conditioning. The results indicate that our proposed method outperforms the baselines in animation quality and expression categorization accuracy. 

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5. Effectiveness of Undergraduate-Generated Animations: Increasing Comprehension and Engagement for Neuroscience Majors and Non-Majors

   https://doi.org/10.1177/00986283211023061  

   Reed, Catherine L.; Hagen, Emilia; Bukach, Cindy M.; Couperus, Jane W. (July 2021, Teaching of Psychology)

   Background: Animations of scientific concepts may improve comprehension by explaining and visualizing the steps of complex processes, but unless they engage student interest in meaningful ways, their effectiveness as teaching tools is limited. We achieve this through a novel approach to animation design that includes the target audience (undergraduates) so that the resultant animations align with their learner characteristics. Objective: This case study investigated whether undergraduate-generated animations were more effective educational tools than informationally equivalent text-and-illustration presentations and whether learners’ background influenced the relative benefits of animations. Method: Incorporating feedback from faculty and undergraduates, we created animations and text-plus-illustration content to explain how neural signals are generated and measured by scalp electrodes. Neuroscience majors and non-majors were presented with either animations or static presentations followed by comprehension and engagement assessments. Results: Both groups showed comprehension and engagement benefits for animations. Although majors showed better overall comprehension, animations improved comprehension for non-majors over static presentations. Conclusion: When educational content is directed for a target audience, animations can be more effective teaching tools for a broader student audience. Teaching Implications: The relevance of online tools for remote instruction makes animations, developed for and by undergraduates, important tools for effectively introducing difficult content. 

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