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of 7
pro vyhledávání: '"Maheshwari, Shubh"'
We introduce MoRAG, a novel multi-part fusion based retrieval-augmented generation strategy for text-based human motion generation. The method enhances motion diffusion models by leveraging additional knowledge obtained through an improved motion ret
Externí odkaz:
http://arxiv.org/abs/2409.12140
We introduce Action-GPT, a plug-and-play framework for incorporating Large Language Models (LLMs) into text-based action generation models. Action phrases in current motion capture datasets contain minimal and to-the-point information. By carefully c
Externí odkaz:
http://arxiv.org/abs/2211.15603
We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion. Our controllable approach enables variable-length generations customizable by action category, ac
Externí odkaz:
http://arxiv.org/abs/2110.11460
Autor:
Gupta, Pranay, Thatipelli, Anirudh, Aggarwal, Aditya, Maheshwari, Shubh, Trivedi, Neel, Das, Sourav, Sarvadevabhatla, Ravi Kiran
In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To study skeleton-action recognition in the wild, we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB video
Externí odkaz:
http://arxiv.org/abs/2007.02072
Akademický článek
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Autor:
Desai, Purva N.
Publikováno v:
National Journal of System & Information Technology (NJSIT); Jun2019, Vol. 12 Issue 1, p15-26, 12p
This book constitutes the refereed proceedings of the 13th International Conference on Information Systems Security, ICISS 2017, held in Mumbai, India, in December 2017. The 17 revised full papers and 7 short papers presented together with 2 invited