Zobrazeno 1 - 10
of 2 288
pro vyhledávání: '"Rhodin, A."'
This paper introduces a method to enhance Interactive Imitation Learning (IIL) by extracting touch interaction points and tracking object movement from video demonstrations. The approach extends current IIL systems by providing robots with detailed k
Externí odkaz:
http://arxiv.org/abs/2411.03555
Autor:
Khoee, Arsham Gholamzadeh, Yu, Yinan, Feldt, Robert, Freimanis, Andris, Rhodin, Patrick Andersson, Parthasarathy, Dhasarathy
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the software release cycle due to their la
Externí odkaz:
http://arxiv.org/abs/2408.09785
For reconstructing high-fidelity human 3D models from monocular videos, it is crucial to maintain consistent large-scale body shapes along with finely matched subtle wrinkles. This paper explores the observation that the per-frame rendering results c
Externí odkaz:
http://arxiv.org/abs/2406.00637
It is now possible to estimate 3D human pose from monocular images with off-the-shelf 3D pose estimators. However, many practical applications require fine-grained absolute pose information for which multi-view cues and camera calibration are necessa
Externí odkaz:
http://arxiv.org/abs/2405.06845
Autor:
Vellenga, Koen, Steinhauer, H. Joe, Karlsson, Alexander, Falkman, Göran, Rhodin, Asli, Koppisetty, Ashok
Driver intention recognition studies increasingly rely on deep neural networks. Deep neural networks have achieved top performance for many different tasks, but it is not a common practice to explicitly analyse the complexity and performance of the n
Externí odkaz:
http://arxiv.org/abs/2402.05150
Neural character models can now reconstruct detailed geometry and texture from video, but they lack explicit shadows and shading, leading to artifacts when generating novel views and poses or during relighting. It is particularly difficult to include
Externí odkaz:
http://arxiv.org/abs/2401.06116
Autor:
Hedlin, Eric, Sharma, Gopal, Mahajan, Shweta, He, Xingzhe, Isack, Hossam, Rhodin, Abhishek Kar Helge, Tagliasacchi, Andrea, Yi, Kwang Moo
Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures, but performance is yet to match the supervised counterpart, making their practicability questionable. We leverage the
Externí odkaz:
http://arxiv.org/abs/2312.00065
Autor:
He, Xingzhe, Cao, Zhiwen, Kolkin, Nicholas, Yu, Lantao, Wan, Kun, Rhodin, Helge, Kalarot, Ratheesh
Large text-to-image models have revolutionized the ability to generate imagery using natural language. However, particularly unique or personal visual concepts, such as pets and furniture, will not be captured by the original model. This has led to i
Externí odkaz:
http://arxiv.org/abs/2311.04315
Human motion capture either requires multi-camera systems or is unreliable when using single-view input due to depth ambiguities. Meanwhile, mirrors are readily available in urban environments and form an affordable alternative by recording two views
Externí odkaz:
http://arxiv.org/abs/2309.04750
It is now possible to reconstruct dynamic human motion and shape from a sparse set of cameras using Neural Radiance Fields (NeRF) driven by an underlying skeleton. However, a challenge remains to model the deformation of cloth and skin in relation to
Externí odkaz:
http://arxiv.org/abs/2308.11951