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In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their past trajectories and other contextual cues.
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Karttikeya Mangalam, Yang An, Harshayu Girase, and Jitendra Malik · 2021
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Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S Refaat, and Benjamin Sapp · 2023
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Liushuai Shi, Le Wang, Sanping Zhou, and Gang Hua · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu · 2023
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Erica Weng, Hana Hoshino, Deva Ramanan, and Kris Kitani · 2023
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Non-probability sampling network for stochastic human trajectory prediction
Inhwan Bae, Jin-Hwi Park, and Hae-Gon Jeon · 2022
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Adversarial diffusion distillation
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Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning
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Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park · 2024
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