Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Hadap, Sunil"'
Autor:
Wu, Haoyu, Karumuri, Meher Gitika, Zou, Chuhang, Bang, Seungbae, Li, Yuelong, Samaras, Dimitris, Hadap, Sunil
Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and
Externí odkaz:
http://arxiv.org/abs/2411.10947
We propose a single-shot approach to determining 6-DoF pose of an object with available 3D computer-aided design (CAD) model from a single RGB image. Our method, dubbed MRC-Net, comprises two stages. The first performs pose classification and renders
Externí odkaz:
http://arxiv.org/abs/2403.08019
Autor:
Karsch, Kevin, Sunkavalli, Kalyan, Hadap, Sunil, Carr, Nathan, Jin, Hailin, Fonte, Rafael, Sittig, Michael
We present a user-friendly image editing system that supports a drag-and-drop object insertion (where the user merely drags objects into the image, and the system automatically places them in 3D and relights them appropriately), post-process illumina
Externí odkaz:
http://arxiv.org/abs/1912.12297
Autor:
Zhang, Jinsong, Sunkavalli, Kalyan, Hold-Geoffroy, Yannick, Hadap, Sunil, Eisenmann, Jonathan, Lalonde, Jean-François
We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN
Externí odkaz:
http://arxiv.org/abs/1906.04909
Publikováno v:
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6908-6917
We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order spherical harmonic representation of the lighting at the given location
Externí odkaz:
http://arxiv.org/abs/1906.03799
Autor:
Yan, Xinchen, Rastogi, Akash, Villegas, Ruben, Sunkavalli, Kalyan, Shechtman, Eli, Hadap, Sunil, Yumer, Ersin, Lee, Honglak
Long-term human motion can be represented as a series of motion modes---motion sequences that capture short-term temporal dynamics---with transitions between them. We leverage this structure and present a novel Motion Transformation Variational Auto-
Externí odkaz:
http://arxiv.org/abs/1808.04545
Autor:
Vo, Minh, Yumer, Ersin, Sunkavalli, Kalyan, Hadap, Sunil, Sheikh, Yaser, Narasimhan, Srinivasa
Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams. To solve this
Externí odkaz:
http://arxiv.org/abs/1805.08717
Autor:
Hold-Geoffroy, Yannick, Sunkavalli, Kalyan, Eisenmann, Jonathan, Fisher, Matt, Gambaretto, Emiliano, Hadap, Sunil, Lalonde, Jean-François
Most current single image camera calibration methods rely on specific image features or user input, and cannot be applied to natural images captured in uncontrolled settings. We propose directly inferring camera calibration parameters from a single i
Externí odkaz:
http://arxiv.org/abs/1712.01259
Real-world lighting often consists of multiple illuminants with different spectra. Separating and manipulating these illuminants in post-process is a challenging problem that requires either significant manual input or calibrated scene geometry and l
Externí odkaz:
http://arxiv.org/abs/1704.05564
Autor:
Shu, Zhixin, Yumer, Ersin, Hadap, Sunil, Sunkavalli, Kalyan, Shechtman, Eli, Samaras, Dimitris
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end gen
Externí odkaz:
http://arxiv.org/abs/1704.04131