Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks

18 May 2017

Papers citing "Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks"

7 / 7 papers shown

Title
MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model Wen-Dao Dai Ling-Hao Chen Jingbo Wang Jinpeng Liu Bo Dai Yansong Tang 52 54 0 31 Dec 2024
BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis Seong-Eun Hong Soobin Lim Juyeong Hwang Minwook Chang Hyeongyeop Kang 85 0 0 28 Nov 2024
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation Jordan Juravsky Yunrong Guo Sanja Fidler Xue Bin Peng AI4CE 16 9 0 15 Jul 2024
MoMask: Generative Masked Modeling of 3D Human Motions Chuan Guo Yuxuan Mu Muhammad Gohar Javed Sen Wang Li Cheng VGen 8 113 0 29 Nov 2023
TEMOS: Generating diverse human motions from textual descriptions Mathis Petrovich Michael J. Black Gül Varol 17 365 0 25 Apr 2022
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,435 0 26 Sep 2016
The KIT Motion-Language Dataset Matthias Plappert Christian Mandery Tamim Asfour 166 267 0 13 Jul 2016