Anindita Ghosh's research while affiliated with Max Planck Institute for Informatics and other places
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Publications (4)
Can we make virtual characters in a scene interact with their surrounding objects through simple instructions? Is it possible to synthesize such motion plausibly with a diverse set of objects and instructions? Inspired by these questions, we present the first framework to synthesize the full‐body motion of virtual human characters performing specif...
"How can we animate 3D-characters from a movie script or move robots by simply telling them what we would like them to do?" "How unstructured and complex can we make a sentence and still generate plausible movements from it?" These are questions that need to be answered in the long-run, as the field is still in its infancy. Inspired by these proble...
Citations
... This line of work focuses on the human body motion and does not generate motions of hand-object interaction. There are methods that generate full-body grasping motion [20,42,59,65], but they are purely data-driven approaches without physics. They either require a post-processing optimization for the object motion [20], or only generate the approaching motion until grasping [59,65]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/272456648753169-1441970135639_Q64/Philipp-Slusallek.jpg)
... However, there are no existing high-quality avatar-text datasets to support supervised text-driven 3D avatar generation. As for animating 3D avatars, a few attempts [Ghosh et al. 2021;Tevet et al. 2022] have been made towards text-driven motion generation by leveraging a motion-text dataset. Nonetheless, restricted by the scarcity of paired motion-text data, those fully supervised methods have limited generalizability. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/519058353541120-1500764563782_Q64/Noshaba-Cheema.jpg)
... [cs.SD] 4 Sep 2023 thesized motion sequence on text conditioned scenario [1] [12] [17] [16] [27] [22] [41] [6] [51] [54], and these methods cover the four major categories of evaluation. Specifically, 1) [1] [12] [27] focus on evaluating the motion fidelity, 2) and [19] [17] [14] [41] [39] propose to evaluate the motion diversity, 3) [16] [27] [22] [41] [6] [51] [54] propose metrics on measuring how semantically consistent the generated motion sequences and the conditioning text descriptions are, and 4) [33] [40] [54] propose protocols on evaluating the quality of motion synthesis from subjective perspective. ...
