Secondary Motion for Performed 2D Animation

Willett, Nora S.; Li, Wilmot; Berthouzoz, Floraine; Finkelstein, Adam

doi:10.1145/3126594.3126641

Cited by 17 publications

(6 citation statements)

References 36 publications

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“…This paper focused on demonstrating generality with respect to the rig and elastic model, rather than the well charted territory of performance optimization. Nonetheless, it would be interesting in future work to see complementary dynamics in a real-time setting and integrated with advanced interfaces or live performance environment [Willett et al 2017], perhaps by leveraging model reduction (cf., ). Adding elastodynamics to an animation pipeline brings with it a host of constraints on input data.…”

Section: Discussionmentioning

confidence: 99%

Complementary dynamics

et al. 2020

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We present a novel approach to enrich arbitrary rig animations with elastodynamic secondary effects. Unlike previous methods which pit rig displacements and physical forces as adversaries against each other, we advocate that physics should complement artists' intentions. We propose optimizing for elastodynamic displacements in the subspace orthogonal to displacements that can be created by the rig. This ensures that the additional dynamic motions do not undo the rig animation. The complementary space is high-dimensional, algebraically constructed without manual oversight, and capable of rich high-frequency dynamics. Unlike prior tracking methods, we do not require extra painted weights, segmentation into fixed and free regions or tracking clusters. Our method is agnostic to the physical model and plugs into non-linear FEM simulations, geometric as-rigid-as-possible energies, or mass-spring models. Our method does not require a particular type of rig and adds secondary effects to skeletal animations, cage-based deformations, wire deformers, motion capture data, and rigid-body simulations.

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Section: Discussionmentioning

confidence: 99%

Complementary dynamics

et al. 2020

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“…Finally, prior work has proposed techniques to automate animation from static materials, including drawing [59,56,60], images [61], slideshows [13,12,66], and comic books [44,9]. Common approaches analyze moving traces and apply movements to subjects that are automatically or manually segmented.…”

Section: Computational Video Editingmentioning

confidence: 99%

Automatic Video Creation From a Web Page

Chi

Sun

Panovich³

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Creating marketing videos from scratch can be challenging, especially when designing for multiple platforms with different viewing criteria. We present URL2Video, an automatic approach that converts a web page into a short video given temporal and visual constraints. URL2Video captures quality materials and design styles extracted from a web page, including fonts, colors, and layouts. Using constraint programming, URL2Video's design engine organizes the visual assets into a sequence of shots and renders to a video with user-specified aspect ratio and duration. Creators can review the video composition, modify constraints, and generate video variation through a user interface. We learned the design process from designers and compared our automatically generated results with their creation through interviews and an online survey. The evaluation shows that URL2Video effectively extracted design elements from a web page and supported designers by bootstrapping the video creation process.

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“…Simulating complex hair strip animations and collisions are somewhat less explored. A potential direction is to add efficient secondary animation authoring [46,43] with our hair geometry modeling.…”

Section: Limitations and Future Workmentioning

confidence: 99%

HairBrush for Immersive Data-Driven Hair Modeling

Nagano¹,

Chen

et al. 2019

Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

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While hair is an essential component of virtual humans, it is also one of the most challenging digital assets to create. Existing automatic techniques lack the generality and flexibility to create rich hair variations, while manual authoring interfaces often require considerable artistic skills and efforts, especially for intricate 3D hair structures that can be difficult to navigate. We propose an interactive hair modeling system that can help create complex hairstyles in minutes or hours that would otherwise take much longer with existing tools. Modelers, including novice users, can focus on the overall hairstyles and local hair deformations, as our system intelligently suggests the desired hair parts. Our method combines the flexibility of manual authoring and the convenience of data-driven automation. Since hair contains intricate 3D structures such as buns, knots, and strands, they are inherently challenging to create using traditional 2D interfaces. Our system provides a new 3D hair authoring interface for immersive interaction in virtual reality (VR). Users can draw high-level guide strips, from which our system predicts the most plausible hairstyles via a deep neural network trained from a professionally curated dataset. Each hairstyle in our dataset is composed of multiple variations, serving as blend-shapes to fit the user drawings via global blending and local deformation. The fitted hair models are visualized as interactive suggestions that the user can select, modify, or ignore. We conducted a user study to confirm that our system can significantly reduce manual labor while improve the output quality for modeling a variety of head and facial hairstyles that are challenging to create via existing techniques.

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Secondary Motion for Performed 2D Animation

Cited by 17 publications

References 36 publications

Complementary dynamics

Complementary dynamics

Automatic Video Creation From a Web Page

HairBrush for Immersive Data-Driven Hair Modeling

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