Interaction patches for multi-character animation

Shum, Hubert P. H.; Komura, Taku; Shiraishi, Masashi; Yamazaki, S.

doi:10.1145/1457515.1409067

Cited by 13 publications

(4 citation statements)

References 18 publications

(9 reference statements)

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“…Results such as characters playing on the slide, sitting and working on desks and interacting with other characters are presented. The use of patch-based approaches are extended to close interaction of characters [Kim et al 2012;Shum et al 2008] and synthesis of movies that follow some scenario [Won et al 2014]. Agrawal and van de Panne [2016] animate close interactions with the environment by aligning the foot step patterns of the motions in the database.…”

Section: Animating Close Interactionsmentioning

confidence: 99%

Neural state machine for character-scene interactions

et al. 2019

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We propose Neural State Machine , a novel data-driven framework to guide characters to achieve goal-driven actions with precise scene interactions. Even a seemingly simple task such as sitting on a chair is notoriously hard to model with supervised learning. This difficulty is because such a task involves complex planning with periodic and non-periodic motions reacting to the scene geometry to precisely position and orient the character. Our proposed deep auto-regressive framework enables modeling of multi-modal scene interaction behaviors purely from data. Given high-level instructions such as the goal location and the action to be launched there, our system computes a series of movements and transitions to reach the goal in the desired state. To allow characters to adapt to a wide range of geometry such as different shapes of furniture and obstacles, we incorporate an efficient data augmentation scheme to randomly switch the 3D geometry while maintaining the context of the original motion. To increase the precision to reach the goal during runtime, we introduce a control scheme that combines egocentric inference and goal-centric inference. We demonstrate the versatility of our model with various scene interaction tasks such as sitting on a chair, avoiding obstacles, opening and entering through a door, and picking and carrying objects generated in real-time just from a single model.

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Section: Animating Close Interactionsmentioning

confidence: 99%

Neural state machine for character-scene interactions

et al. 2019

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“…By further obtaining the potential future actions between the characters through game tree expansion, the best actions are selected by the min‐max algorithm. Such close interaction patterns can be precomputed and stored as Interaction Patches [SKSY08] for synthesizing new multi‐character scenes by concatenating different patches. To simulate hit‐and‐react interactions, momentum‐based inverse kinematics [KHL05] is proposed to edit pre‐recorded motions according to the strength of the external perturbation and the point of contact on the body.…”

Section: Related Workmentioning

confidence: 99%

“…An intuitive solution will be combining individually captured motions [SKY07, SKSY08, KHL05] and editing the interaction according to additional constraints given by the users. However, careful design of the motion editing algorithms (such as [HKT10, HK09]) and parameter tuning are required to avoid interpenetration of body parts.…”

Section: Introductionmentioning

confidence: 99%

Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi‐Hot Class Embedding

Goel

Men

2022

Computer Graphics Forum

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Synthesizing multi‐character interactions is a challenging task due to the complex and varied interactions between the characters. In particular, precise spatiotemporal alignment between characters is required in generating close interactions such as dancing and fighting. Existing work in generating multi‐character interactions focuses on generating a single type of reactive motion for a given sequence which results in a lack of variety of the resultant motions. In this paper, we propose a novel way to create realistic human reactive motions which are not presented in the given dataset by mixing and matching different types of close interactions. We propose a Conditional Hierarchical Generative Adversarial Network with Multi‐Hot Class Embedding to generate the Mix and Match reactive motions of the follower from a given motion sequence of the leader. Experiments are conducted on both noisy (depth‐based) and high‐quality (MoCap‐based) interaction datasets. The quantitative and qualitative results show that our approach outperforms the state‐of‐the‐art methods on the given datasets. We also provide an augmented dataset with realistic reactive motions to stimulate future research in this area.

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“…Some recent work, including [63] and [64], uses motion-patches to create populated scenes of pedestrians. These methods create agents as needed to fill motion scripts and do not contain agents exploring shared spaces and planning/interacting as they accomplish disparate goals.…”

Section: Future Workmentioning

confidence: 99%

Menge: A Modular Framework for Simulating Crowd Movement

2016

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We present Menge, a cross-platform, extensible, modular framework for simulating pedestrian movement in a crowd. Menge's architecture is inspired by an implicit decomposition of the problem of simulating crowds into component subproblems. These subproblems can typically be solved in many ways; different combinations of subproblem solutions yield crowd simulators with likewise varying properties. Menge creates abstractions for those subproblems and provides a plug-in architecture so that a novel simulator can be dynamically configured by connecting built-in and bespoke implementations of solutions to the various subproblems. Use of this type of framework could facilitate crowd simulation research, evaluation, and applications by reducing the cost of entering the domain, facilitating collaboration, and making comparisons between algorithms simpler. We show how the Menge framework is compatible with many prior models and algorithms used in crowd simulation and illustrate its flexibility via a varied set of scenarios and applications.

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Interaction patches for multi-character animation

Cited by 13 publications

References 18 publications

Neural state machine for character-scene interactions

Neural state machine for character-scene interactions

Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi‐Hot Class Embedding

Menge: A Modular Framework for Simulating Crowd Movement

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