Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Hong, Susung"'
The fields of 3D reconstruction and text-based 3D editing have advanced significantly with the evolution of text-based diffusion models. While existing 3D editing methods excel at modifying color, texture, and style, they struggle with extensive geom
Externí odkaz:
http://arxiv.org/abs/2412.05279
Diffusion models have emerged as a powerful tool for generating high-quality images, videos, and 3D content. While sampling guidance techniques like CFG improve quality, they reduce diversity and motion. Autoguidance mitigates these issues but demand
Externí odkaz:
http://arxiv.org/abs/2411.18664
Autor:
Hong, Susung
Conditional diffusion models have shown remarkable success in visual content generation, producing high-quality samples across various domains, largely due to classifier-free guidance (CFG). Recent attempts to extend guidance to unconditional models
Externí odkaz:
http://arxiv.org/abs/2408.00760
3D reconstruction from multi-view images is one of the fundamental challenges in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising technique capable of real-time rendering with high-quality 3D reconstruct
Externí odkaz:
http://arxiv.org/abs/2406.11672
Autor:
Seo, Junyoung, Hong, Susung, Jang, Wooseok, Kim, Inès Hyeonsu, Kwak, Minseop, Lee, Doyup, Kim, Seungryong
Text-to-3D generation has achieved significant success by incorporating powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to the inconsistency of 3D geometry. Recently, since large-scale multi-view datasets have been releas
Externí odkaz:
http://arxiv.org/abs/2402.02972
In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) generation. Despite their effectiveness, these frameworks face challenges
Externí odkaz:
http://arxiv.org/abs/2305.14330
Existing score-distilling text-to-3D generation techniques, despite their considerable promise, often encounter the view inconsistency problem. One of the most notable issues is the Janus problem, where the most canonical view of an object (\textit{e
Externí odkaz:
http://arxiv.org/abs/2303.15413
Generative models have recently undergone significant advancement due to the diffusion models. The success of these models can be often attributed to their use of guidance techniques, such as classifier or classifier-free guidance, which provide effe
Externí odkaz:
http://arxiv.org/abs/2212.08861
Autor:
Hong, Sunghwan, Nam, Jisu, Cho, Seokju, Hong, Susung, Jeon, Sangryul, Min, Dongbo, Kim, Seungryong
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters. This architecture, however, inevitably results in a low-resolution match
Externí odkaz:
http://arxiv.org/abs/2210.02689
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-conditional diffusion guidance methods, such as classifier and classifi
Externí odkaz:
http://arxiv.org/abs/2210.00939