30 citations · 100 across the 6 of their papers we have counts for
7 papers
Text-Guided Generation and Editing of Compositional 3D Avatars
Hao Zhang, Yao Feng, Peter Kulits +3
Our goal is to create a realistic 3D facial avatar with hair and accessories using only a text description. While this challenge has attracted significant recent interest, existing…
Learning Disentangled Avatars with Hybrid 3D Representations
Yao Feng, Weiyang Liu, Timo Bolkart +3
Tremendous efforts have been made to learn animatable and photorealistic human avatars. Towards this end, both explicit and implicit 3D representations are heavily studied for a ho…
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
Zeju Qiu, Weiyang Liu, Haiwen Feng +6
Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models t…
MeshDiffusion: Score-based Generative 3D Mesh Modeling
Zhen Liu, Yao Feng, Michael J. Black +3
We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other…
Capturing and Animation of Body and Clothing from Monocular Video
Yao Feng, Jinlong Yang, Marc Pollefeys +2
While recent work has shown progress on extracting clothed 3D human avatars from a single image, video, or a set of 3D scans, several limitations remain. Most methods use a holisti…
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
Yao Feng, Haiwen Feng, Michael J. Black +1
While current monocular 3D face reconstruction methods can recover fine geometric details, they suffer several limitations. Some methods produce faces that cannot be realistically…
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
Sijia Liu, Songtao Lu, Xiangyi Chen +5
In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective fun…