Zobrazeno 1 - 10
of 2 296
pro vyhledávání: '"Leonidas J"'
Autor:
Nakayama, Kiyohiro, Ackermann, Jan, Kesdogan, Timur Levent, Zheng, Yang, Korosteleva, Maria, Sorkine-Hornung, Olga, Guibas, Leonidas J., Yang, Guandao, Wetzstein, Gordon
Apparel is essential to human life, offering protection, mirroring cultural identities, and showcasing personal style. Yet, the creation of garments remains a time-consuming process, largely due to the manual work involved in designing them. To simpl
Externí odkaz:
http://arxiv.org/abs/2412.03937
Large-scale vision foundation models such as Segment Anything (SAM) demonstrate impressive performance in zero-shot image segmentation at multiple levels of granularity. However, these zero-shot predictions are rarely 3D-consistent. As the camera vie
Externí odkaz:
http://arxiv.org/abs/2405.19678
Autor:
Pan, Boxiao, Xu, Zhan, Huang, Chun-Hao Paul, Singh, Krishna Kumar, Zhou, Yang, Guibas, Leonidas J., Yang, Jimei
Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community. This task involves synthesizing background that aligns with the motion and appearance of the foreground
Externí odkaz:
http://arxiv.org/abs/2401.10822
Neural radiance fields (NeRFs) have gained popularity with multiple works showing promising results across various applications. However, to the best of our knowledge, existing works do not explicitly model the distribution of training camera poses,
Externí odkaz:
http://arxiv.org/abs/2401.08140
Given the difficulty of manually annotating motion in video, the current best motion estimation methods are trained with synthetic data, and therefore struggle somewhat due to a train/test gap. Self-supervised methods hold the promise of training dir
Externí odkaz:
http://arxiv.org/abs/2401.00850
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:36958-36977, 2023
Some extremely low-dimensional yet crucial geometric eigen-lengths often determine the success of some geometric tasks. For example, the height of an object is important to measure to check if it can fit between the shelves of a cabinet, while the wi
Externí odkaz:
http://arxiv.org/abs/2312.15610
Autor:
Kim, Kunho, Uy, Mikaela Angelina, Paschalidou, Despoina, Jacobson, Alec, Guibas, Leonidas J., Sung, Minhyuk
We propose OptCtrlPoints, a data-driven framework designed to identify the optimal sparse set of control points for reproducing target shapes using biharmonic 3D shape deformation. Control-point-based 3D deformation methods are widely utilized for in
Externí odkaz:
http://arxiv.org/abs/2309.12899
We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos w
Externí odkaz:
http://arxiv.org/abs/2307.15055
Autor:
Nakayama, Kiyohiro, Uy, Mikaela Angelina, Huang, Jiahui, Hu, Shi-Min, Li, Ke, Guibas, Leonidas J
While the community of 3D point cloud generation has witnessed a big growth in recent years, there still lacks an effective way to enable intuitive user control in the generation process, hence limiting the general utility of such methods. Since an i
Externí odkaz:
http://arxiv.org/abs/2305.01921
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data. Generating NeRFs, however, remains difficult in many scenarios. For instance, training a NeRF with only a small number of
Externí odkaz:
http://arxiv.org/abs/2304.14473