Zobrazeno 1 - 10
of 367
pro vyhledávání: '"ZHANG Yunzhi"'
Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., "identity-preserving g
Externí odkaz:
http://arxiv.org/abs/2411.18616
We introduce the Scene Language, a visual scene representation that concisely and precisely describes the structure, semantics, and identity of visual scenes. It represents a scene with three key components: a program that specifies the hierarchical
Externí odkaz:
http://arxiv.org/abs/2410.16770
Autor:
Zhang, Yunzhi, Li, Zizhang, Raj, Amit, Engelhardt, Andreas, Li, Yuanzhen, Hou, Tingbo, Wu, Jiajun, Jampani, Varun
We propose 3D Congealing, a novel problem of 3D-aware alignment for 2D images capturing semantically similar objects. Given a collection of unlabeled Internet images, our goal is to associate the shared semantic parts from the inputs and aggregate th
Externí odkaz:
http://arxiv.org/abs/2404.02125
Autor:
Engelhardt, Andreas, Raj, Amit, Boss, Mark, Zhang, Yunzhi, Kar, Abhishek, Li, Yuanzhen, Sun, Deqing, Brualla, Ricardo Martin, Barron, Jonathan T., Lensch, Hendrik P. A., Jampani, Varun
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background. Inverse rendering of an object based on unconstrained image collections i
Externí odkaz:
http://arxiv.org/abs/2401.10171
Autor:
Li, Zizhang, Litvak, Dor, Li, Ruining, Zhang, Yunzhi, Jakab, Tomas, Rupprecht, Christian, Wu, Shangzhe, Vedaldi, Andrea, Wu, Jiajun
Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species
Externí odkaz:
http://arxiv.org/abs/2401.02400
We introduce a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion synthesis, our model requires no pose annotations or parametric shape models for tr
Externí odkaz:
http://arxiv.org/abs/2312.13604
Our understanding of the visual world is centered around various concept axes, characterizing different aspects of visual entities. While different concept axes can be easily specified by language, e.g. color, the exact visual nuances along each axis
Externí odkaz:
http://arxiv.org/abs/2312.03587
Autor:
Lee, Tony, Yasunaga, Michihiro, Meng, Chenlin, Mai, Yifan, Park, Joon Sung, Gupta, Agrim, Zhang, Yunzhi, Narayanan, Deepak, Teufel, Hannah Benita, Bellagente, Marco, Kang, Minguk, Park, Taesung, Leskovec, Jure, Zhu, Jun-Yan, Fei-Fei, Li, Wu, Jiajun, Ermon, Stefano, Liang, Percy
The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption. However, we lack a comprehensive quantitative understanding of their capabilities and risks. To fill this gap, we introduce a new
Externí odkaz:
http://arxiv.org/abs/2311.04287
Autor:
Sargent, Kyle, Li, Zizhang, Shah, Tanmay, Herrmann, Charles, Yu, Hong-Xing, Zhang, Yunzhi, Chan, Eric Ryan, Lagun, Dmitry, Fei-Fei, Li, Sun, Deqing, Wu, Jiajun
We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds, we propose new techniques to address challenges introduced
Externí odkaz:
http://arxiv.org/abs/2310.17994
We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cas
Externí odkaz:
http://arxiv.org/abs/2310.16044