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pro vyhledávání: '"YE, YUTING"'
Animating human-scene interactions such as pick-and-place tasks in cluttered, complex layouts is a challenging task, with objects of a wide variation of geometries and articulation under scenarios with various obstacles. The main difficulty lies in t
Externí odkaz:
http://arxiv.org/abs/2412.06702
Autor:
Hong, Fangzhou, Guzov, Vladimir, Kim, Hyo Jin, Ye, Yuting, Newcombe, Richard, Liu, Ziwei, Ma, Lingni
As the prevalence of wearable devices, learning egocentric motions becomes essential to develop contextual AI. In this work, we present EgoLM, a versatile framework that tracks and understands egocentric motions from multi-modal inputs, e.g., egocent
Externí odkaz:
http://arxiv.org/abs/2409.18127
Autor:
Guzov, Vladimir, Jiang, Yifeng, Hong, Fangzhou, Pons-Moll, Gerard, Newcombe, Richard, Liu, C. Karen, Ye, Yuting, Ma, Lingni
This paper investigates the online generation of realistic full-body human motion using a single head-mounted device with an outward-facing color camera and the ability to perform visual SLAM. Given the inherent ambiguity of this setup, we introduce
Externí odkaz:
http://arxiv.org/abs/2409.13426
We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike existing methods using an overly sparse high-dimensional latent, we p
Externí odkaz:
http://arxiv.org/abs/2407.18946
Autor:
Ma, Lingni, Ye, Yuting, Hong, Fangzhou, Guzov, Vladimir, Jiang, Yifeng, Postyeni, Rowan, Pesqueira, Luis, Gamino, Alexander, Baiyya, Vijay, Kim, Hyo Jin, Bailey, Kevin, Fosas, David Soriano, Liu, C. Karen, Liu, Ziwei, Engel, Jakob, De Nardi, Renzo, Newcombe, Richard
We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) full-body ground-truth motion; b) multiple multimodal egocentric data fr
Externí odkaz:
http://arxiv.org/abs/2406.09905
Autor:
Lin, Xiaobin, Wei, Maoliang, Lei, Kunhao, Wang, Zijia, Wang, Chi, Ma, Hui, Ye, Yuting, Zhan, Qiwei, Li, Da, Dai, Shixun, Zhang, Baile, Hu, Xiaoyong, Li, Lan, Li, Erping, Lin, Hongtao
On-chip structured light, with potentially infinite complexity, has emerged as a linchpin in the realm of integrated photonics. However, the realization of arbitrarily tailoring a multitude of light field dimensions in complex media remains a challen
Externí odkaz:
http://arxiv.org/abs/2405.18666
Full-body avatar presence is crucial for immersive social and environmental interactions in digital reality. However, current devices only provide three six degrees of freedom (DOF) poses from the headset and two controllers (i.e. three-point tracker
Externí odkaz:
http://arxiv.org/abs/2402.09211
Transforming neutral, characterless input motions to embody the distinct style of a notable character in real time is highly compelling for character animation. This paper introduces MOCHA, a novel online motion characterization framework that transf
Externí odkaz:
http://arxiv.org/abs/2310.10079
Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports, and healthcare, for motion prediction and data augmentation. Recent
Externí odkaz:
http://arxiv.org/abs/2309.13742
Autor:
Zhong, Chuyu, Liao, Kun, Dai, Tianxiang, Wei, Maoliang, Ma, Hui, Wu, Jianghong, Zhang, Zhibin, Ye, Yuting, Luo, Ye, Chen, Zequn, Jian, Jialing, Sun, Chulei, Tang, Bo, Zhang, Peng, Liu, Ruonan, Li, Junying, Yang, Jianyi, Li, Lan, Liu, Kaihui, Hu, Xiaoyong, Lin, Hongtao
Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances a
Externí odkaz:
http://arxiv.org/abs/2307.06882