5 citations · 14 across the 7 of their papers we have counts for
8 papers
HairShifter: Consistent and High-Fidelity Video Hair Transfer via Anchor-Guided Animation
Wangzheng Shi, Yinglin Zheng, Yuxin Lin +3
Hair transfer is increasingly valuable across domains such as social media, gaming, advertising, and entertainment. While significant progress has been made in single-image hair tr…
Diffusion Models without Classifier-free Guidance
Zhicong Tang, Jianmin Bao, Dong Chen +1
This paper presents Model-guidance (MG), a novel objective for training diffusion model that addresses and removes of the commonly used Classifier-free guidance (CFG). Our innovati…
Improved Noise Schedule for Diffusion Training
Tiankai Hang, Shuyang Gu, Xin Geng +1
Diffusion models have emerged as the de facto choice for generating high-quality visual signals across various domains. However, training a single model to predict noise across var…
VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder
Zhicong Tang, Shuyang Gu, Chunyu Wang +4
This paper introduces a pioneering 3D volumetric encoder designed for text-to-3D generation. To scale up the training data for the diffusion model, a lightweight network is develop…
Towards More Unified In-context Visual Understanding
Dianmo Sheng, Dongdong Chen, Zhentao Tan +7
The rapid advancement of large language models (LLMs) has accelerated the emergence of in-context learning (ICL) as a cutting-edge approach in the natural language processing domai…
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
Zigang Geng, Binxin Yang, Tiankai Hang +8
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge…
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
Xiaoyi Dong, Jianmin Bao, Yinglin Zheng +9
This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. The core id…
General Facial Representation Learning in a Visual-Linguistic Manner
Yinglin Zheng, Hao Yang, Ting Zhang +7
How to learn a universal facial representation that boosts all face analysis tasks? This paper takes one step toward this goal. In this paper, we study the transfer performance of…