no code implementations • ICML 2020 • Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly
In this paper, we study the problem of constrained min-max optimization in a black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values.
no code implementations • 7 May 2024 • Jing Lin, Yao Feng, Weiyang Liu, Michael J. Black
The novel features of ChatHuman include leveraging academic publications to guide the application of 3D human-related tools, employing a retrieval-augmented generation model to generate in-context-learning examples for handling new tools, and discriminating and integrating tool results to enhance 3D human understanding.
1 code implementation • 25 Apr 2024 • Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Yao Feng, Michael J. Black
We address the problem of regressing 3D human pose and shape from a single image, with a focus on 3D accuracy.
no code implementations • 27 Mar 2024 • Keyu Wu, Lingchen Yang, Zhiyi Kuang, Yao Feng, Xutao Han, Yuefan Shen, Hongbo Fu, Kun Zhou, Youyi Zheng
For the interior, we employ a data-driven, multi-view 3D hair reconstruction method.
no code implementations • 30 Nov 2023 • Yao Feng, Jing Lin, Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Michael J. Black
Additionally, ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses, leading to two advanced tasks: speculative pose generation and reasoning about pose estimation.
1 code implementation • 10 Nov 2023 • Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf
We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT).
no code implementations • 23 Oct 2023 • Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull, Michael J. Black, Bernhard Schölkopf
Recent work has focused on the former, and methods for reconstructing open surfaces do not support fast reconstruction with material and lighting or unconditional generative modelling.
2 code implementations • ICCV 2023 • Yandong Wen, Weiyang Liu, Yao Feng, Bhiksha Raj, Rita Singh, Adrian Weller, Michael J. Black, Bernhard Schölkopf
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL).
no code implementations • 13 Sep 2023 • Hao Zhang, Yao Feng, Peter Kulits, Yandong Wen, Justus Thies, Michael J. Black
We argue that existing methods are limited because they employ a monolithic modeling approach, using a single representation for the head, face, hair, and accessories.
no code implementations • 12 Sep 2023 • Yao Feng, Weiyang Liu, Timo Bolkart, Jinlong Yang, Marc Pollefeys, Michael J. Black
Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e. g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata.
no code implementations • NeurIPS 2023 • Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf
To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks.
1 code implementation • 14 Mar 2023 • Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, Weiyang Liu
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.
1 code implementation • 15 Nov 2022 • Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm.
no code implementations • 2 Nov 2022 • Yao Feng, Yuhong Jiang, Hang Su, Dong Yan, Jun Zhu
Model-based reinforcement learning usually suffers from a high sample complexity in training the world model, especially for the environments with complex dynamics.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • 4 Oct 2022 • Yao Feng, Jinlong Yang, Marc Pollefeys, Michael J. Black, Timo Bolkart
Building on this insight, we propose SCARF (Segmented Clothed Avatar Radiance Field), a hybrid model combining a mesh-based body with a neural radiance field.
1 code implementation • 11 May 2021 • Yao Feng, Vasileios Choutas, Timo Bolkart, Dimitrios Tzionas, Michael J. Black
Second, human shape is highly correlated with gender, but existing work ignores this.
2 code implementations • 7 Dec 2020 • Yao Feng, Haiwen Feng, Michael J. Black, Timo Bolkart
Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression.
1 code implementation • 30 Sep 2019 • Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Minyi Hong, Una-May O'Reilly
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 function but may query its values.
2 code implementations • 25 Mar 2019 • Pengfei Yao, Zheng Fang, Fan Wu, Yao Feng, Jiwei Li
Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data.
4 code implementations • ECCV 2018 • Yao Feng, Fan Wu, Xiaohu Shao, Yan-Feng Wang, Xi Zhou
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Ranked #1 on 3D Face Reconstruction on Florence