Zobrazeno 1 - 10
of 286
pro vyhledávání: '"Liu, Ming Yu"'
Meshes are fundamental representations of 3D surfaces. However, creating high-quality meshes is a labor-intensive task that requires significant time and expertise in 3D modeling. While a delicate object often requires over $10^4$ faces to be accurat
Externí odkaz:
http://arxiv.org/abs/2412.09548
Autor:
NVIDIA, Bala, Maciej, Cui, Yin, Ding, Yifan, Ge, Yunhao, Hao, Zekun, Hasselgren, Jon, Huffman, Jacob, Jin, Jingyi, Lewis, J. P., Li, Zhaoshuo, Lin, Chen-Hsuan, Lin, Yen-Chen, Lin, Tsung-Yi, Liu, Ming-Yu, Luo, Alice, Ma, Qianli, Munkberg, Jacob, Shi, Stella, Wei, Fangyin, Xiang, Donglai, Xu, Jiashu, Zeng, Xiaohui, Zhang, Qinsheng
We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation. Our method first synthesizes RGB and surface normal images of the described object at multiple viewpoints using a diffusion model. The multi-view observations
Externí odkaz:
http://arxiv.org/abs/2411.07135
Autor:
NVIDIA, Atzmon, Yuval, Bala, Maciej, Balaji, Yogesh, Cai, Tiffany, Cui, Yin, Fan, Jiaojiao, Ge, Yunhao, Gururani, Siddharth, Huffman, Jacob, Isaac, Ronald, Jannaty, Pooya, Karras, Tero, Lam, Grace, Lewis, J. P., Licata, Aaron, Lin, Yen-Chen, Liu, Ming-Yu, Ma, Qianli, Mallya, Arun, Martino-Tarr, Ashlee, Mendez, Doug, Nah, Seungjun, Pruett, Chris, Reda, Fitsum, Song, Jiaming, Wang, Ting-Chun, Wei, Fangyin, Zeng, Xiaohui, Zeng, Yu, Zhang, Qinsheng
We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in wh
Externí odkaz:
http://arxiv.org/abs/2411.07126
Autor:
Wang, Zhendong, Li, Zhaoshuo, Mandlekar, Ajay, Xu, Zhenjia, Fan, Jiaojiao, Narang, Yashraj, Fan, Linxi, Zhu, Yuke, Balaji, Yogesh, Zhou, Mingyuan, Liu, Ming-Yu, Zeng, Yu
Diffusion models, praised for their success in generative tasks, are increasingly being applied to robotics, demonstrating exceptional performance in behavior cloning. However, their slow generation process stemming from iterative denoising steps pos
Externí odkaz:
http://arxiv.org/abs/2410.21257
Autor:
Tang, Jiaxiang, Li, Zhaoshuo, Hao, Zekun, Liu, Xian, Zeng, Gang, Liu, Ming-Yu, Zhang, Qinsheng
Current auto-regressive mesh generation methods suffer from issues such as incompleteness, insufficient detail, and poor generalization. In this paper, we propose an Auto-regressive Auto-encoder (ArAE) model capable of generating high-quality 3D mesh
Externí odkaz:
http://arxiv.org/abs/2409.18114
Masked diffusion models (MDMs) have emerged as a popular research topic for generative modeling of discrete data, thanks to their superior performance over other discrete diffusion models, and are rivaling the auto-regressive models (ARMs) for langua
Externí odkaz:
http://arxiv.org/abs/2409.02908
Autor:
Li, Boyi, Zhu, Ligeng, Tian, Ran, Tan, Shuhan, Chen, Yuxiao, Lu, Yao, Cui, Yin, Veer, Sushant, Ehrlich, Max, Philion, Jonah, Weng, Xinshuo, Xue, Fuzhao, Tao, Andrew, Liu, Ming-Yu, Fidler, Sanja, Ivanovic, Boris, Darrell, Trevor, Malik, Jitendra, Han, Song, Pavone, Marco
We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging complementary strengths of Vision Language Models (VLMs). By utilizing bot
Externí odkaz:
http://arxiv.org/abs/2407.18908
Autor:
Zeng, Yu, Patel, Vishal M., Wang, Haochen, Huang, Xun, Wang, Ting-Chun, Liu, Ming-Yu, Balaji, Yogesh
Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes, finding applications in various domains. To achieve the personalization capability, existing methods rely on finetu
Externí odkaz:
http://arxiv.org/abs/2407.06187
Existing automatic captioning methods for visual content face challenges such as lack of detail, content hallucination, and poor instruction following. In this work, we propose VisualFactChecker (VFC), a flexible training-free pipeline that generates
Externí odkaz:
http://arxiv.org/abs/2404.19752
We present Condition-Aware Neural Network (CAN), a new method for adding control to image generative models. In parallel to prior conditional control methods, CAN controls the image generation process by dynamically manipulating the weight of the neu
Externí odkaz:
http://arxiv.org/abs/2404.01143