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I am currently pursuing Masters in Computer Vision (MSCV) at Robotics Institute at Carnegie Mellon University, advised by Prof. Lazlo A. Jeni and Michael Zollhoefer, Meta Reality Labs, where I am working on dynamic 3D reconstruction focusing non-rigidly deforming scenes captured by RGBD sensors. My primary interests lie in the intersection of Computer Vision and Machine Learning. Prior to joining CMU, I worked as a Software Engineer at Microsoft (R&D) Pvt. Ltd., where my work focused on building agile deployment and experimentation of ML models for spam/phish detection during mail flow for Microsoft Defender for Office (MDO). I had the previledge of working with a team of talented engineers and researchers, and was mentored by Jay Goyal, Saurabh Shrivastav and Ganesh Pande. In 2018, I worked under the supervision of Dr. Amarjot Singh and Dr S.N Omkar in the Computational Intelligence lab at Indian Institute of Science, Bangalore suspicious activity detection in surveillance using gait-based gender recognition system for subjects in loosely fitted clothing. I completed my undergraduate education with a Major in Computer Science and Engineering from National Institute of Technology, Warangal in 2021. I had the pleasure of working under Dr. R Padmavathy for my undergraduate thesis. In the late summer of 2020, I was a part of the Software Product Sprint at Google APAC, where I worked under the mentorship of Yash Jain. I have had the opportunity to intern at Microsoft Teams Mobile team, and The Garage at Microsoft (R&D) Pvt. Ltd., in the summer of 2020 and 2019 respectively. |
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Carnegie Mellon University |
Microsoft (R&D) Pvt. Ltd.
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National Institute of Technology,
Warangal |
Google APAC |
Indian Institute of Science, Bengaluru |
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[poster] We streamline 3D asset creation by editing existing scenes, reducing complexity and time. While NeRF offers detailed rendering, it's hard to manipulate. Instead, 3D Gaussian Splatting enables real-time rendering, simplifying tasks. Our architecture integrates vision language, allowing language-driven editing of visual properties. This bridges the gap between language understanding and 3D scene editing, enhancing workflows. |
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[project page] Our methodology can work with any text-to-image (T2I) diffusion architectures pre-trained on large image dataset. We introduce a spatiotemporal attention and background regularization techniques along with one-shot tuning strategy within our "GIF-Tune" model for temporally coherent and depth-consistent GIFs. |
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[arVix] [slides] Alpha-MEPOL method for unsupervised RL in multiple environments achieves enhanced performance with dynamic alpha and a higher KL-Divergence threshold. Curiosity-driven exploration proves effective in high-dimensional environments, fostering diverse experiences and improving learning outcomes. Our experiments confirm positive results, highlighting the effectiveness of these modifications in the current context. |
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[pdf] Addressing the challenge of identifying individuals wearing loosely fitted clothes of the opposite gender to evade detection, our proposed Bayesian Gait-based Gender Identification (BGGI) excels in real-world scenarios. Utilizing the LFCI dataset for training, our technique demonstrates superior performance in both pose estimation and gender recognition, employing state-of-the-art methods on dense real-world videos. |
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[Aug 2023] Starting my M.S. Computer Vision (MSCV) degree at the Robotics Institute, Carnegie Mellon
University (CMU). [Jun 2021] Started a new position as a Software Engineer, Security, Compliance and Management Org, Microsoft, Hyderabad, India. [May 2021] Received my Bachelor's degree from National Institute of Technology Warangal, with a major in Computer Science and Engineering. [May 2020] Started summer internship in the Microsoft Teams Mobile team at Microsoft, Hyderabad, India [May 2019] Started summer internship in the The Garage at Microsoft, Hyderabad, India [Feb 2019] Started a new position as Programmer at G-Bit Studios [May 2018] Started summer internship at Indian Institute of Science, Bangalore |
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Source code from Jon Barron
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