Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Dutt, Niladri Shekhar"'
Autor:
Muralikrishnan, Sanjeev, Dutt, Niladri Shekhar, Chaudhuri, Siddhartha, Aigerman, Noam, Kim, Vladimir, Fisher, Matthew, Mitra, Niloy J.
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, an
Externí odkaz:
http://arxiv.org/abs/2407.14958
We present Diff3F as a simple, robust, and class-agnostic feature descriptor that can be computed for untextured input shapes (meshes or point clouds). Our method distills diffusion features from image foundational models onto input shapes. Specifica
Externí odkaz:
http://arxiv.org/abs/2311.17024
Neural Radiance Fields (NeRFs) have recently emerged as a popular option for photo-realistic object capture due to their ability to faithfully capture high-fidelity volumetric content even from handheld video input. Although much research has been de
Externí odkaz:
http://arxiv.org/abs/2310.09965
Autor:
Dutt, Niladri Shekhar, Patel, Sunil
Generative Adversarial Networks GANs are algorithmic architectures that use two neural networks, pitting one against the opposite so as to come up with new, synthetic instances of data that can pass for real data. Training a GAN is a challenging prob
Externí odkaz:
http://arxiv.org/abs/2011.13728
Autor:
Kashif Naseer Qureshi, Thomas Newe
This book is devoted to the new standards, technologies, and communication systems for Artificial Intelligence of Things (AIoT) networks. Smart and intelligent communication networks have gained significant attention due to the combination of AI and