Zobrazeno 1 - 10
of 19 586
pro vyhledávání: '"Fidler, A"'
Autor:
Chen, Ziyu, Yang, Jiawei, Huang, Jiahui, de Lutio, Riccardo, Esturo, Janick Martinez, Ivanovic, Boris, Litany, Or, Gojcic, Zan, Fidler, Sanja, Pavone, Marco, Song, Li, Wang, Yue
We introduce OmniRe, a holistic approach for efficiently reconstructing high-fidelity dynamic urban scenes from on-device logs. Recent methods for modeling driving sequences using neural radiance fields or Gaussian Splatting have demonstrated the pot
Externí odkaz:
http://arxiv.org/abs/2408.16760
Autor:
Liang, Ruofan, Gojcic, Zan, Nimier-David, Merlin, Acuna, David, Vijaykumar, Nandita, Fidler, Sanja, Wang, Zian
The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong
Externí odkaz:
http://arxiv.org/abs/2408.09702
Autor:
Li, Boyi, Zhu, Ligeng, Tian, Ran, Tan, Shuhan, Chen, Yuxiao, Lu, Yao, Cui, Yin, Veer, Sushant, Ehrlich, Max, Philion, Jonah, Weng, Xinshuo, Xue, Fuzhao, Tao, Andrew, Liu, Ming-Yu, Fidler, Sanja, Ivanovic, Boris, Darrell, Trevor, Malik, Jitendra, Han, Song, Pavone, Marco
We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging complementary strengths of Vision Language Models (VLMs). By utilizing bot
Externí odkaz:
http://arxiv.org/abs/2407.18908
Physically-simulated models for human motion can generate high-quality responsive character animations, often in real-time. Natural language serves as a flexible interface for controlling these models, allowing expert and non-expert users to quickly
Externí odkaz:
http://arxiv.org/abs/2407.10481
Autor:
Moenne-Loccoz, Nicolas, Mirzaei, Ashkan, Perel, Or, de Lutio, Riccardo, Esturo, Janick Martinez, State, Gavriel, Fidler, Sanja, Sharp, Nicholas, Gojcic, Zan
Particle-based representations of radiance fields such as 3D Gaussian Splatting have found great success for reconstructing and re-rendering of complex scenes. Most existing methods render particles via rasterization, projecting them to screen space
Externí odkaz:
http://arxiv.org/abs/2407.07090
Autor:
Williams, Francis, Huang, Jiahui, Swartz, Jonathan, Klár, Gergely, Thakkar, Vijay, Cong, Matthew, Ren, Xuanchi, Li, Ruilong, Fuji-Tsang, Clement, Fidler, Sanja, Sifakis, Eftychios, Museth, Ken
We present fVDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. fVDB provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, at
Externí odkaz:
http://arxiv.org/abs/2407.01781
Autor:
Wang, Letian, Kim, Seung Wook, Yang, Jiawei, Yu, Cunjun, Ivanovic, Boris, Waslander, Steven L., Wang, Yue, Fidler, Sanja, Pavone, Marco, Karkus, Peter
We propose DistillNeRF, a self-supervised learning framework addressing the challenge of understanding 3D environments from limited 2D observations in autonomous driving. Our method is a generalizable feedforward model that predicts a rich neural sce
Externí odkaz:
http://arxiv.org/abs/2406.12095
Autor:
Ren, Jiawei, Xie, Kevin, Mirzaei, Ashkan, Liang, Hanxue, Zeng, Xiaohui, Kreis, Karsten, Liu, Ziwei, Torralba, Antonio, Fidler, Sanja, Kim, Seung Wook, Ling, Huan
We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Key to our success is a novel dataset of multiview videos containing cur
Externí odkaz:
http://arxiv.org/abs/2406.10324
Autor:
Zhang, Dongsu, Williams, Francis, Gojcic, Zan, Kreis, Karsten, Fidler, Sanja, Kim, Young Min, Kar, Amlan
We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans, abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion, we aim to extrapolate fine geometry from unlabeled and beyond spatial lim
Externí odkaz:
http://arxiv.org/abs/2406.08292
We present NeRF-XL, a principled method for distributing Neural Radiance Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering of NeRFs with an arbitrarily large capacity. We begin by revisiting existing multi-GPU approaches,
Externí odkaz:
http://arxiv.org/abs/2404.16221