no code implementations • 1 May 2024 • Saurabh Saini, Kapil Ahuja, Siddartha Chennareddy, Karthik Boddupalli
A competitive approach for type classification on the same dataset achieved 81%-91% performance.
no code implementations • CVPR 2024 • Saurabh Saini, P J Narayanan
We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition.
no code implementations • CVPR 2023 • Rahul Goel, Dhawal Sirikonda, Saurabh Saini, PJ Narayanan
Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it.
no code implementations • 19 Dec 2022 • Rahul Goel, Sirikonda Dhawal, Saurabh Saini, P. J. Narayanan
In this work, we present StyleTRF, a compact, quick-to-optimize strategy for stylized view generation using TensoRF.
1 code implementation • ICCV 2021 • Hsin-Ping Huang, Hung-Yu Tseng, Saurabh Saini, Maneesh Singh, Ming-Hsuan Yang
Second, we develop point cloud aggregation modules to gather the style information of the 3D scene, and then modulate the features in the point cloud with a linear transformation matrix.
1 code implementation • 26 Aug 2019 • Aakash KT, Parikshit Sakurikar, Saurabh Saini, P. J. Narayanan
Photo realism in computer generated imagery is crucially dependent on how well an artist is able to recreate real-world materials in the scene.
Graphics
no code implementations • 11 Feb 2019 • Saurabh Saini, P. J. Narayanan
Global context priors establish correspondences at the scene level.
no code implementations • 13 Dec 2016 • Aditya Singh, Saurabh Saini, Rajvi Shah, PJ Narayanan
In this paper, we study the problem of generating relevant and useful hash-tags for short video clips.
no code implementations • 18 Oct 2016 • Aditya Singh, Saurabh Saini, Rajvi Shah, P. J. Narayanan
In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets.