Zobrazeno 1 - 10
of 601
pro vyhledávání: '"Li Weiyu"'
Autor:
Li, Mengfei, Long, Xiaoxiao, Liang, Yixun, Li, Weiyu, Liu, Yuan, Li, Peng, Luo, Wenhan, Wang, Wenping, Guo, Yike
Despite recent advancements in the Large Reconstruction Model (LRM) demonstrating impressive results, when extending its input from single image to multiple images, it exhibits inefficiencies, subpar geometric and texture quality, as well as slower c
Externí odkaz:
http://arxiv.org/abs/2406.07648
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interac
Externí odkaz:
http://arxiv.org/abs/2405.14979
Robustness to malicious attacks is of paramount importance for distributed learning. Existing works often consider the classical Byzantine attacks model, which assumes that some workers can send arbitrarily malicious messages to the server and distur
Externí odkaz:
http://arxiv.org/abs/2404.13647
Animatable 3D reconstruction has significant applications across various fields, primarily relying on artists' handcraft creation. Recently, some studies have successfully constructed animatable 3D models from monocular videos. However, these approac
Externí odkaz:
http://arxiv.org/abs/2403.11427
It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and thus lack 3D knowledge during the lifting, leading to the multi-view in
Externí odkaz:
http://arxiv.org/abs/2310.02596
This paper studies Byzantine-robust stochastic optimization over a decentralized network, where every agent periodically communicates with its neighbors to exchange local models, and then updates its own local model by stochastic gradient descent (SG
Externí odkaz:
http://arxiv.org/abs/2308.05292
We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual a
Externí odkaz:
http://arxiv.org/abs/2306.00378
We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, ren
Externí odkaz:
http://arxiv.org/abs/2304.12670
This paper studies the relationship between state feedback policies and disturbance response policies for the standard Linear Quadratic Regulator (LQR). For open-loop stable plants, we establish a simple relationship between the optimal state feedbac
Externí odkaz:
http://arxiv.org/abs/2304.03831
The homogeneity, or more generally, the similarity between source domains and a target domain seems to be essential to a positive transfer learning. In practice, however, the similarity condition is difficult to check and is often violated. In this p
Externí odkaz:
http://arxiv.org/abs/2302.11222