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Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics, gaming, animation, and the metaverse.
Signature verification using a ”siamese” time delay neural network
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Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Action-conditioned 3d human motion synthesis with transformer vae
Mathis Petrovich, Michael J Black, and Gül Varol · 2021
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Learning transferable visual models
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Teach: Temporal action composition for 3d humans
Nikos Athanasiou, Mathis Petrovich, Michael J Black, and Gül Varol · 2022
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Vector quantized diffusion model for text-to-image synthesis
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo · 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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Motionclip: Exposing human motion generation to clip space
Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or · 2022
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Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
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Yonatan Shafir, Guy Tevet, Roy Kapon, and Amit H Bermano · 2023
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Role-Aware Interaction Generation from Textual Description
Mikihiro Tanaka and Kent Fujiwara · 2023
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Human motion diffusion model
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Jonathan Tseng, Rodrigo Castellon, and Karen Liu · 2023
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Nikos Athanasiou, Mathis Petrovich, Michael J Black, and Gül Varol · 2023
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Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel · 2023
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German Barquero, Sergio Escalera, and Cristina Palmero · 2023
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German Barquero, Sergio Escalera, and Cristina Palmero · 2024
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MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
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