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Aditya Aggarwal
Researcher at International Institute of Information Technology, Hyderabad
Publications - 5
Citations - 42
Aditya Aggarwal is an academic researcher from International Institute of Information Technology, Hyderabad. The author has contributed to research in topics: 3D reconstruction & Rendering (computer graphics). The author has an hindex of 2, co-authored 5 publications receiving 6 citations.
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Quo Vadis, Skeleton Action Recognition ?
Pranay Gupta,Anirudh Thatipelli,Aditya Aggarwal,Shubh Maheshwari,Neel Trivedi,Sourav Das,Ravi Kiran Sarvadevabhatla +6 more
TL;DR: The results from benchmarking the top performers of NTU-120 on Skeletics-152 reveal the challenges and domain gap induced by actions 'in the wild', and proposes new frontiers for human action recognition.
Journal ArticleDOI
Quo Vadis, Skeleton Action Recognition?
Pranay Gupta,Anirudh Thatipelli,Aditya Aggarwal,Shubh Maheshwari,Neel Trivedi,Sourav Das,Ravi Kiran Sarvadevabhatla +6 more
TL;DR: Skeleton-Mimetics-152 as discussed by the authors is a 3D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset, and Metaphorics, a dataset with caption style annotated YouTube videos of the popular social game Dumb Charades and interpretative dance performances.
Posted Content
Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image
TL;DR: This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image, and demonstrates that the approach—dubbed reconstruct, rasterize and backprop (RRB)—achieves significantly lower pose estimation errors compared to prior art, and is able to recover dense object shapes and poses from imagery.
Proceedings ArticleDOI
A principled formulation of integrating objects in Monocular SLAM
TL;DR: This work presents a new SLAM framework in which they use monocular edge based SLAM, along with category level models, to localize objects in the scene as well as improve the camera trajectory, object poses along with its shape and 3D structure.
Proceedings ArticleDOI
Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image
TL;DR: In this article, Li et al. presented a new system to obtain dense object reconstructions along with 6-DoF poses from a single image by leveraging recent advances in differentiable rendering (in particular, rasterization) to close the loop with 3D reconstruction in camera frame.