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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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