Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Tewari, Ayush"'
This paper focuses on studying the configuration spaces of graphs realised in $\mathbb C^2$, such that the configuration space is, after normalisation, one dimensional. If this is the case, then the configuration space is, generically, a smooth compl
Externí odkaz:
http://arxiv.org/abs/2408.00449
Autor:
Gupta, Vinayak, Girish, Rongali Simhachala Venkata, T, Mukund Varma, Tewari, Ayush, Mitra, Kaushik
Neural rendering methods can achieve near-photorealistic image synthesis of scenes from posed input images. However, when the images are imperfect, e.g., captured in very low-light conditions, state-of-the-art methods fail to reconstruct high-quality
Externí odkaz:
http://arxiv.org/abs/2407.08221
This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence. Our method performs per-video gradient-descent minimization of a simple least-s
Externí odkaz:
http://arxiv.org/abs/2404.15259
Autor:
Tewari, Ayush Kumar
We focus on checking the validity of the half-plane property on two prominent classes of transversal matroids, namely lattice path matroids and bicircular matroids. We show that lattice path matroids satisfy the half-plane property. Subsequently, we
Externí odkaz:
http://arxiv.org/abs/2402.06302
Autor:
Lyu, Linjie, Tewari, Ayush, Habermann, Marc, Saito, Shunsuke, Zollhöfer, Michael, Leimkühler, Thomas, Theobalt, Christian
Inverse rendering, the process of inferring scene properties from images, is a challenging inverse problem. The task is ill-posed, as many different scene configurations can give rise to the same image. Most existing solutions incorporate priors into
Externí odkaz:
http://arxiv.org/abs/2310.00362
Autor:
Tewari, Ayush Kumar
Our work is motivated by the connection established between Lorentzian polynomials and the Dressian in the seminal work of Br\"and\'en and Huh on Lorentzian polynomials. We analyze this relation for the class of positroids, and are able to show that
Externí odkaz:
http://arxiv.org/abs/2309.17091
Autor:
O'Connell, Thomas P., Bonnen, Tyler, Friedman, Yoni, Tewari, Ayush, Tenenbaum, Josh B., Sitzmann, Vincent, Kanwisher, Nancy
Humans effortlessly infer the 3D shape of objects. What computations underlie this ability? Although various computational models have been proposed, none of them capture the human ability to match object shape across viewpoints. Here, we ask whether
Externí odkaz:
http://arxiv.org/abs/2308.11300
We introduce the notion of lattice path matroidal subdivisions, or LPM subdivisions for short, and show that these subdivisions are regular and hence the weight vectors for them lie in the Dressian. This leads us to explore the structure of the set o
Externí odkaz:
http://arxiv.org/abs/2307.04180
Autor:
Tewari, Ayush, Yin, Tianwei, Cazenavette, George, Rezchikov, Semon, Tenenbaum, Joshua B., Durand, Frédo, Freeman, William T., Sitzmann, Vincent
Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenarios where training samples are readily available, which is not always th
Externí odkaz:
http://arxiv.org/abs/2306.11719
Autor:
Mendiratta, Mohit, Pan, Xingang, Elgharib, Mohamed, Teotia, Kartik, R, Mallikarjun B, Tewari, Ayush, Golyanik, Vladislav, Kortylewski, Adam, Theobalt, Christian
Capturing and editing full head performances enables the creation of virtual characters with various applications such as extended reality and media production. The past few years witnessed a steep rise in the photorealism of human head avatars. Such
Externí odkaz:
http://arxiv.org/abs/2306.00547