Zobrazeno 1 - 10
of 844
pro vyhledávání: '"Chen, Change"'
Garment animation is ubiquitous in various applications, such as virtual reality, gaming, and film producing. Recently, learning-based approaches obtain compelling performance in animating diverse garments under versatile scenarios. Nevertheless, to
Externí odkaz:
http://arxiv.org/abs/2501.01393
Video restoration poses non-trivial challenges in maintaining fidelity while recovering temporally consistent details from unknown degradations in the wild. Despite recent advances in diffusion-based restoration, these methods often face limitations
Externí odkaz:
http://arxiv.org/abs/2501.01320
Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model generation are restricted to low resolutions with suboptimal multi-view con
Externí odkaz:
http://arxiv.org/abs/2412.18565
In this work, we introduce GauSim, a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. Unlike traditional methods that treat kernels as particles within
Externí odkaz:
http://arxiv.org/abs/2412.17804
This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Predictio
Externí odkaz:
http://arxiv.org/abs/2412.09013
This study aims to achieve more precise and versatile object control in image-to-video (I2V) generation. Current methods typically represent the spatial movement of target objects with 2D trajectories, which often fail to capture user intention and f
Externí odkaz:
http://arxiv.org/abs/2412.07721
In this work, we introduce a single parameter $\omega$, to effectively control granularity in diffusion-based synthesis. This parameter is incorporated during the denoising steps of the diffusion model's reverse process. Our approach does not require
Externí odkaz:
http://arxiv.org/abs/2411.17769
Autor:
Lan, Yushi, Zhou, Shangchen, Lyu, Zhaoyang, Hong, Fangzhou, Yang, Shuai, Dai, Bo, Pan, Xingang, Loy, Chen Change
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges,
Externí odkaz:
http://arxiv.org/abs/2411.08033
Line art colorization plays a crucial role in hand-drawn animation production, where digital artists manually colorize segments using a paint bucket tool, guided by RGB values from character color design sheets. This process, often called paint bucke
Externí odkaz:
http://arxiv.org/abs/2410.19424
Autor:
Jiang, Yuming, Zhao, Nanxuan, Liu, Qing, Singh, Krishna Kumar, Yang, Shuai, Loy, Chen Change, Liu, Ziwei
Group portrait editing is highly desirable since users constantly want to add a person, delete a person, or manipulate existing persons. It is also challenging due to the intricate dynamics of human interactions and the diverse gestures. In this work
Externí odkaz:
http://arxiv.org/abs/2409.14379