Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Bhattad, Anand"'
Autor:
Zhang, Zitian, Fortier-Chouinard, Frédéric, Garon, Mathieu, Bhattad, Anand, Lalonde, Jean-François
We present ZeroComp, an effective zero-shot 3D object compositing approach that does not require paired composite-scene images during training. Our method leverages ControlNet to condition from intrinsic images and combines it with a Stable Diffusion
Externí odkaz:
http://arxiv.org/abs/2410.08168
Autor:
Zhang, Xiao, Gao, William, Jain, Seemandhar, Maire, Michael, Forsyth, David. A., Bhattad, Anand
Image relighting is the task of showing what a scene from a source image would look like if illuminated differently. Inverse graphics schemes recover an explicit representation of geometry and a set of chosen intrinsics, then relight with some form o
Externí odkaz:
http://arxiv.org/abs/2405.21074
We introduce Videoshop, a training-free video editing algorithm for localized semantic edits. Videoshop allows users to use any editing software, including Photoshop and generative inpainting, to modify the first frame; it automatically propagates th
Externí odkaz:
http://arxiv.org/abs/2403.14617
Generative models excel at mimicking real scenes, suggesting they might inherently encode important intrinsic scene properties. In this paper, we aim to explore the following key questions: (1) What intrinsic knowledge do generative models like GANs,
Externí odkaz:
http://arxiv.org/abs/2311.17137
Autor:
Sarkar, Ayush, Mai, Hanlin, Mahapatra, Amitabh, Lazebnik, Svetlana, Forsyth, D. A., Bhattad, Anand
Generative models can produce impressively realistic images. This paper demonstrates that generated images have geometric features different from those of real images. We build a set of collections of generated images, prequalified to fool simple, si
Externí odkaz:
http://arxiv.org/abs/2311.17138
Dense depth and surface normal predictors should possess the equivariant property to cropping-and-resizing -- cropping the input image should result in cropping the same output image. However, we find that state-of-the-art depth and normal predictors
Externí odkaz:
http://arxiv.org/abs/2309.16646
Autor:
Michel, Oscar, Bhattad, Anand, VanderBilt, Eli, Krishna, Ranjay, Kembhavi, Aniruddha, Gupta, Tanmay
Existing image editing tools, while powerful, typically disregard the underlying 3D geometry from which the image is projected. As a result, edits made using these tools may become detached from the geometry and lighting conditions that are at the fo
Externí odkaz:
http://arxiv.org/abs/2307.11073
We present Blocks2World, a novel method for 3D scene rendering and editing that leverages a two-step process: convex decomposition of images and conditioned synthesis. Our technique begins by extracting 3D parallelepipeds from various objects in a gi
Externí odkaz:
http://arxiv.org/abs/2307.03847
Autor:
Marathe, Kalyani, Bigverdi, Mahtab, Khan, Nishat, Kundu, Tuhin, Howe, Patrick, S, Sharan Ranjit, Bhattad, Anand, Kembhavi, Aniruddha, Shapiro, Linda G., Krishna, Ranjay
Dense pixel-specific representation learning at scale has been bottlenecked due to the unavailability of large-scale multi-view datasets. Current methods for building effective pretraining datasets heavily rely on annotated 3D meshes, point clouds, a
Externí odkaz:
http://arxiv.org/abs/2306.15128
Autor:
Lin, Zhi-Hao, Liu, Bohan, Chen, Yi-Ting, Chen, Kuan-Sheng, Forsyth, David, Huang, Jia-Bin, Bhattad, Anand, Wang, Shenlong
We present UrbanIR (Urban Scene Inverse Rendering), a new inverse graphics model that enables realistic, free-viewpoint renderings of scenes under various lighting conditions with a single video. It accurately infers shape, albedo, visibility, and su
Externí odkaz:
http://arxiv.org/abs/2306.09349