Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Tomar, Snehal Singh"'
Extracting Implicit Neural Representations (INRs) on video data poses unique challenges due to the additional temporal dimension. In the context of videos, INRs have predominantly relied on a frame-only parameterization, which sacrifices the spatiote
Externí odkaz:
http://arxiv.org/abs/2406.19299
With the metaverse slowly becoming a reality and given the rapid pace of developments toward the creation of digital humans, the need for a principled style editing pipeline for human faces is bound to increase manifold. We cater to this need by intr
Externí odkaz:
http://arxiv.org/abs/2312.15037
The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed
Externí odkaz:
http://arxiv.org/abs/2211.11224
With an unprecedented increase in the number of agents and systems that aim to navigate the real world using visual cues and the rising impetus for 3D Vision Models, the importance of depth estimation is hard to understate. While supervised methods r
Externí odkaz:
http://arxiv.org/abs/2211.11066
With the advent of an increasing number of Augmented and Virtual Reality applications that aim to perform meaningful and controlled style edits on images of human faces, the impetus for the task of parsing face images to produce accurate and fine-gra
Externí odkaz:
http://arxiv.org/abs/2207.01871