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Conference Paper: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations

TitleTask-Oriented Human-Object Interactions Generation with Implicit Neural Representations
Authors
Keywords3D
3D computer vision
Algorithms
Algorithms
etc
Generative models for image
video
Issue Date2024
Citation
Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, 2024, p. 3023-3032 How to Cite?
AbstractDigital human motion synthesis is a vibrant research field with applications in movies, AR/VR, and video games. Whereas methods were proposed to generate natural and realistic human motions, most only focus on modeling humans and largely ignore object movements. Generating task-oriented human-object interaction motions in simulation is challenging. For different intents of using the objects, humans conduct various motions, which requires the human first to approach the objects and then make them move consistently with the human instead of staying still. Also, to deploy in downstream applications, the synthesized motions are desired to be flexible in length, providing options to personalize the predicted motions for various purposes. To this end, we propose TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations, which generates full human-object interaction motions to conduct specific tasks, given only the task type, the object, and a starting human status. TOHO generates human-object motions in four steps: 1) it first estimates the object's final position given the task intent; 2) it then generates keyframe poses grasping the objects; 3) after that, it infills the keyframes and generates continuous motions; 4) finally, it applies a compact closed-form object motion estimation to generate the object motion. Our method generates continuous motions that are parameterized only by the temporal coordinate, which allows for upsampling of the sequence to arbitrary frames and adjusting the motion speeds by designing the temporal coordinate vector. This work takes a step further toward general human-scene interaction simulation.
Persistent Identifierhttp://hdl.handle.net/10722/352425

 

DC FieldValueLanguage
dc.contributor.authorLi, Quanzhou-
dc.contributor.authorWang, Jingbo-
dc.contributor.authorLoy, Chen Change-
dc.contributor.authorDai, Bo-
dc.date.accessioned2024-12-16T03:58:52Z-
dc.date.available2024-12-16T03:58:52Z-
dc.date.issued2024-
dc.identifier.citationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, 2024, p. 3023-3032-
dc.identifier.urihttp://hdl.handle.net/10722/352425-
dc.description.abstractDigital human motion synthesis is a vibrant research field with applications in movies, AR/VR, and video games. Whereas methods were proposed to generate natural and realistic human motions, most only focus on modeling humans and largely ignore object movements. Generating task-oriented human-object interaction motions in simulation is challenging. For different intents of using the objects, humans conduct various motions, which requires the human first to approach the objects and then make them move consistently with the human instead of staying still. Also, to deploy in downstream applications, the synthesized motions are desired to be flexible in length, providing options to personalize the predicted motions for various purposes. To this end, we propose TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations, which generates full human-object interaction motions to conduct specific tasks, given only the task type, the object, and a starting human status. TOHO generates human-object motions in four steps: 1) it first estimates the object's final position given the task intent; 2) it then generates keyframe poses grasping the objects; 3) after that, it infills the keyframes and generates continuous motions; 4) finally, it applies a compact closed-form object motion estimation to generate the object motion. Our method generates continuous motions that are parameterized only by the temporal coordinate, which allows for upsampling of the sequence to arbitrary frames and adjusting the motion speeds by designing the temporal coordinate vector. This work takes a step further toward general human-scene interaction simulation.-
dc.languageeng-
dc.relation.ispartofProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024-
dc.subject3D-
dc.subject3D computer vision-
dc.subjectAlgorithms-
dc.subjectAlgorithms-
dc.subjectetc-
dc.subjectGenerative models for image-
dc.subjectvideo-
dc.titleTask-Oriented Human-Object Interactions Generation with Implicit Neural Representations-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/WACV57701.2024.00301-
dc.identifier.scopuseid_2-s2.0-85189459735-
dc.identifier.spage3023-
dc.identifier.epage3032-

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