arXiv subjects

Anik Gupta

Publications and source records attributed to Anik Gupta.

5 recordsLinked to original sources

arXiv2026

Ego-1K -- A Large-Scale Multiview Video Dataset for Egocentric Vision

We present Ego-1K, a large-scale collection of time-synchronized egocentric multiview videos designed to advance neural 3D video synthesis and dynamic scene understanding. The dataset contains nearly 1,000 short egocentric videos captured with a custom rig with 12 synchronized cameras surrounding a 4-camera VR headset worn by the user. Scene content focuses on hand motions and hand-object interactions in different settings. We describe rig design, data processing, and calibration. Our dataset enables new ways to benchmark egocentric scene reconstruction methods, an important research area as smart glasses with multiple cameras become omnipresent. Our experiments demonstrate that our dataset presents unique challenges for existing 3D and 4D novel view synthesis methods due to large disparities and image motion caused by close dynamic objects and rig egomotion. Our dataset supports future research in this challenging domain. It is available at https://huggingface.co/datasets/facebook/ego-1k.

cs.CV↗

arXiv2025

Dreamcrafter: Immersive Editing of 3D Radiance Fields Through Flexible, Generative Inputs and Outputs

Authoring 3D scenes is a central task for spatial computing applications. Competing visions for lowering existing barriers are (1) focus on immersive, direct manipulation of 3D content or (2) leverage AI techniques that capture real scenes (3D Radiance Fields such as, NeRFs, 3D Gaussian Splatting) and modify them at a higher level of abstraction, at the cost of high latency. We unify the complementary strengths of these approaches and investigate how to integrate generative AI advances into real-time, immersive 3D Radiance Field editing. We introduce Dreamcrafter, a VR-based 3D scene editing system that: (1) provides a modular architecture to integrate generative AI algorithms; (2) combines different levels of control for creating objects, including natural language and direct manipulation; and (3) introduces proxy representations that support interaction during high-latency operations. We contribute empirical findings on control preferences and discuss how generative AI interfaces beyond text input enhance creativity in scene editing and world building.

cs.HC↗

arXiv2012

Energy Efficient Algorithms and Power Consumption Techniques in High Performance Computing

High Performance Computing is an internet based computing which makes computer infrastructure and services available to the user for research purpose. However, an important issue which needs to be resolved before High Performance Computing Cluster with large pool of servers gain widespread acceptance is the design of data centers with less energy consumption. It is only possible when servers produce less heat and consume less power. Systems reliability decreases with increase in temperature due to heat generation caused by large power consumption as computing in high temperature is more error-prone. Here in this paper our approach is to design and implement a high performance cluster for high-end research in the High Energy Physics stream. This involves the usage of fine grained power gating technique in microprocessors and energy efficient algorithms that reduce the overall running cost of the data center.

cs.DC↗

arXiv2012

HEP Analysis Facility An Approach to Grid Computing

HEP Analysis Facility is a cluster designed and implemented in Scientific Linux Cern 5.5 to grant High Energy Physics researchers one place where they can go to undertake a particular task or to provide a parallel processing architecture in which CPU resources are shared across a network and all machines function as one large supercomputer.

cs.DC↗

arXiv2012

Hep Cluster First Step Towards Grid Computing

HEP Cluster is designed and implemented in Scientific Linux Cern 5.5 to grant High Energy Physics researchers one place where they can go to undertake a particular task or to provide a parallel processing architecture in which CPU resources are shared across a network and all machines function as one large supercomputer. It gives physicists a facility to access computers and data, transparently, without having to consider location, operating system, account administration, and other details. By using this facility researchers can process their jobs much faster than the stand alone desktop systems. Keywords: Cluster, Network, Storage, Parallel Computing & Gris.

cs.DC↗