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Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far.
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Statistical mutual conversion between whole body motion primitives and linguistic sentences for human motions
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Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
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The kit motion-language dataset
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Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks
Matthias Plappert, Christian Mandery, and Tamim Asfour · 2018
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Paired recurrent autoencoders for bidirectional translation between robot actions and linguistic descriptions
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Silvia Zuffi, Angjoo Kanazawa, and Michael J. Black · 2018
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Chaitanya Ahuja and Louis-Philippe Morency · 2019
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Expressive body capture: 3d hands, face, and body from a single image
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Language models are unsupervised multitask learners
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Generating diverse high-fidelity images with vq-vae-2
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Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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TEMOS: Generating diverse human motions from textual descriptions
Mathis Petrovich, Michael J. Black, and Gül Varol · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Opt: Open pre-trained transformer language models
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Imagebind: One embedding space to bind them all
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Human motion diffusion as a generative prior
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