Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Laina, Iro"'
Autor:
Ohtani, Go, Tadokoro, Ryu, Yamada, Ryosuke, Asano, Yuki M., Laina, Iro, Rupprecht, Christian, Inoue, Nakamasa, Yokota, Rio, Kataoka, Hirokatsu, Aoki, Yoshimitsu
In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate
Externí odkaz:
http://arxiv.org/abs/2409.00768
In this paper, we introduce Splatt3R, a pose-free, feed-forward method for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs. Given uncalibrated natural images, Splatt3R can predict 3D Gaussian Splats without requiring any came
Externí odkaz:
http://arxiv.org/abs/2408.13912
Autor:
Bhalgat, Yash, Tschernezki, Vadim, Laina, Iro, Henriques, João F., Vedaldi, Andrea, Zisserman, Andrew
Egocentric videos present unique challenges for 3D scene understanding due to rapid camera motion, frequent object occlusions, and limited object visibility. This paper introduces a novel approach to instance segmentation and tracking in first-person
Externí odkaz:
http://arxiv.org/abs/2408.09860
Autor:
Nakamura, Ryo, Tadokoro, Ryu, Yamada, Ryosuke, Asano, Yuki M., Laina, Iro, Rupprecht, Christian, Inoue, Nakamasa, Yokota, Rio, Kataoka, Hirokatsu
Pre-training and transfer learning are an important building block of current computer vision systems. While pre-training is usually performed on large real-world image datasets, in this paper we ask whether this is truly necessary. To this end, we s
Externí odkaz:
http://arxiv.org/abs/2408.00677
Autor:
Ma, Xianzheng, Bhalgat, Yash, Smart, Brandon, Chen, Shuai, Li, Xinghui, Ding, Jian, Gu, Jindong, Chen, Dave Zhenyu, Peng, Songyou, Bian, Jia-Wang, Torr, Philip H, Pollefeys, Marc, Nießner, Matthias, Reid, Ian D, Chang, Angel X., Laina, Iro, Prisacariu, Victor Adrian
As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overvie
Externí odkaz:
http://arxiv.org/abs/2405.10255
3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames with exist
Externí odkaz:
http://arxiv.org/abs/2404.19758
We consider the problem of editing 3D objects and scenes based on open-ended language instructions. A common approach to this problem is to use a 2D image generator or editor to guide the 3D editing process, obviating the need for 3D data. However, t
Externí odkaz:
http://arxiv.org/abs/2404.18929
Understanding complex scenes at multiple levels of abstraction remains a formidable challenge in computer vision. To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a sin
Externí odkaz:
http://arxiv.org/abs/2403.10997
Autor:
Melas-Kyriazi, Luke, Laina, Iro, Rupprecht, Christian, Neverova, Natalia, Vedaldi, Andrea, Gafni, Oran, Kokkinos, Filippos
Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation is to fine-tune the
Externí odkaz:
http://arxiv.org/abs/2402.08682
We propose a novel feed-forward 3D editing framework called Shap-Editor. Prior research on editing 3D objects primarily concentrated on editing individual objects by leveraging off-the-shelf 2D image editing networks. This is achieved via a process c
Externí odkaz:
http://arxiv.org/abs/2312.09246