Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Wan, Ziyu"'
We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively convert
Externí odkaz:
http://arxiv.org/abs/2408.03178
Autor:
Sun, Qi, Guo, Zhiyang, Wan, Ziyu, Yan, Jing Nathan, Yin, Shengming, Zhou, Wengang, Liao, Jing, Li, Houqiang
In recent years, the increasing demand for dynamic 3D assets in design and gaming applications has given rise to powerful generative pipelines capable of synthesizing high-quality 4D objects. Previous methods generally rely on score distillation samp
Externí odkaz:
http://arxiv.org/abs/2405.18132
Language models as intelligent agents push the boundaries of sequential decision-making agents but struggle with limited knowledge of environmental dynamics and exponentially huge action space. Recent efforts like GLAM and TWOSOME manually constrain
Externí odkaz:
http://arxiv.org/abs/2405.15821
NeRF (Neural Radiance Fields) has demonstrated tremendous potential in novel view synthesis and 3D reconstruction, but its performance is sensitive to input image quality, which struggles to achieve high-fidelity rendering when provided with low-qual
Externí odkaz:
http://arxiv.org/abs/2404.03654
Autor:
Feng, Xidong, Wan, Ziyu, Yang, Mengyue, Wang, Ziyan, Koushik, Girish A., Du, Yali, Wen, Ying, Wang, Jun
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks. However, RL is often hindered by issues such as low sample efficiency, lack of interpretability, and sparse supervision signals. To tackle thes
Externí odkaz:
http://arxiv.org/abs/2402.07157
This work presents HeadArtist for 3D head generation from text descriptions. With a landmark-guided ControlNet serving as the generative prior, we come up with an efficient pipeline that optimizes a parameterized 3D head model under the supervision o
Externí odkaz:
http://arxiv.org/abs/2312.07539
Autor:
Wan, Ziyu, Paschalidou, Despoina, Huang, Ian, Liu, Hongyu, Shen, Bokui, Xiang, Xiaoyu, Liao, Jing, Guibas, Leonidas
The increased demand for 3D data in AR/VR, robotics and gaming applications, gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these models rely on the Score Distillation Sampling (SDS) algorithm to o
Externí odkaz:
http://arxiv.org/abs/2312.06663
Autor:
Feng, Xidong, Wan, Ziyu, Wen, Muning, McAleer, Stephen Marcus, Wen, Ying, Zhang, Weinan, Wang, Jun
Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step reasoning. These methods rely on prompting a pre-trained model to serve as
Externí odkaz:
http://arxiv.org/abs/2309.17179
Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with simple ge
Externí odkaz:
http://arxiv.org/abs/2305.11588
Autor:
Wan, Ziyu, Richardt, Christian, Božič, Aljaž, Li, Chao, Rengarajan, Vijay, Nam, Seonghyeon, Xiang, Xiaoyu, Li, Tuotuo, Zhu, Bo, Ranjan, Rakesh, Liao, Jing
Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations - for each pixel. This is prohibitively exp
Externí odkaz:
http://arxiv.org/abs/2304.10537