Zobrazeno 1 - 10
of 821
pro vyhledávání: '"Saito Jun"'
Autor:
Qin, Dafei, Lin, Hongyang, Zhang, Qixuan, Qiao, Kaichun, Zhang, Longwen, Zhao, Zijun, Saito, Jun, Yu, Jingyi, Xu, Lan, Komura, Taku
We propose GauFace, a novel Gaussian Splatting representation, tailored for efficient animation and rendering of physically-based facial assets. Leveraging strong geometric priors and constrained optimization, GauFace ensures a neat and structured Ga
Externí odkaz:
http://arxiv.org/abs/2409.07441
Autor:
Han, Xingjian, Jiang, Yu, Wang, Weiming, Fang, Guoxin, Gill, Simeon, Zhang, Zhiqiang, Wang, Shengfa, Saito, Jun, Kumar, Deepak, Luo, Zhongxuan, Whiting, Emily, Wang, Charlie C. L.
Joint injuries, and their long-term consequences, present a substantial global health burden. Wearable prophylactic braces are an attractive potential solution to reduce the incidence of joint injuries by limiting joint movements that are related to
Externí odkaz:
http://arxiv.org/abs/2408.16659
The physical plausibility of human motions is vital to various applications in fields including but not limited to graphics, animation, robotics, vision, biomechanics, and sports science. While fully simulating human motions with physics is an extrem
Externí odkaz:
http://arxiv.org/abs/2310.03930
We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild. Our approach, called Neural Face Rigging (NFR), holds three key properties: (i) NFR's expression space maintains human-inte
Externí odkaz:
http://arxiv.org/abs/2305.08296
Publikováno v:
EPJ Web of Conferences, Vol 175, p 11020 (2018)
We propose a lattice field theory formulation which overcomes some fundamental diffculties in realizing exact supersymmetry on the lattice. The Leibniz rule for the difference operator can be recovered by defining a new product on the lattice, the st
Externí odkaz:
https://doaj.org/article/49e6572969b449619d3cd7a9c0cb535a
We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformations on fixed or topology-equivalent skeleton templates, our method focu
Externí odkaz:
http://arxiv.org/abs/2208.00790
Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from
Externí odkaz:
http://arxiv.org/abs/2207.11524
We present an implicit neural representation to learn the spatio-temporal space of kinematic motions. Unlike previous work that represents motion as discrete sequential samples, we propose to express the vast motion space as a continuous function ove
Externí odkaz:
http://arxiv.org/abs/2206.03287
Autor:
Aigerman, Noam, Gupta, Kunal, Kim, Vladimir G., Chaudhuri, Siddhartha, Saito, Jun, Groueix, Thibault
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well
Externí odkaz:
http://arxiv.org/abs/2205.02904