22 citations · 93 across the 18 of their papers we have counts for
18 papers
HPSv3: Towards Wide-Spectrum Human Preference Score
Yuhang Ma, Yunhao Shui, Xiaoshi Wu +2
Evaluating text-to-image generation models requires alignment with human perception, yet existing human-centric metrics are constrained by limited data coverage, suboptimal feature…
Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models
Fu-Yun Wang, Yunhao Shui, Jingtan Piao +2
Diffusion models have made substantial advances in image generation, yet models trained on large, unfiltered datasets often yield outputs misaligned with human preferences. Numerou…
Self-NPO: Data-Free Diffusion Model Enhancement via Truncated Diffusion Fine-Tuning
Fu-Yun Wang, Keqiang Sun, Yao Teng +4
Diffusion models have demonstrated remarkable success in various visual generation tasks, including image, video, and 3D content generation. Preference optimization (PO) is a promi…
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events
Yijin Li, Yichen Shen, Zhaoyang Huang +9
Recent advances in event-based vision suggest that these systems complement traditional cameras by providing continuous observation without frame rate limitations and a high dynami…
GenCA: A Text-conditioned Generative Model for Realistic and Drivable Codec Avatars
Keqiang Sun, Amin Jourabloo, Riddhish Bhalodia +9
Photo-realistic and controllable 3D avatars are crucial for various applications such as virtual and mixed reality (VR/MR), telepresence, gaming, and film production. Traditional m…
Phased Consistency Models
Fu-Yun Wang, Zhaoyang Huang, Alexander William Bergman +9
Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image ge…
Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models
Xiaoshi Wu, Yiming Hao, Manyuan Zhang +5
Optimizing a text-to-image diffusion model with a given reward function is an important but underexplored research area. In this study, we propose Deep Reward Tuning (DRTune), an a…
ECNet: Effective Controllable Text-to-Image Diffusion Models
Sicheng Li, Keqiang Sun, Zhixin Lai +5
The conditional text-to-image diffusion models have garnered significant attention in recent years. However, the precision of these models is often compromised mainly for two reaso…
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
Fu-Yun Wang, Zhaoyang Huang, Weikang Bian +5
This paper introduces an effective method for computation-efficient personalized style video generation without requiring access to any personalized video data. It reduces the nece…
Ponymation: Learning Articulated 3D Animal Motions from Unlabeled Online Videos
Keqiang Sun, Dor Litvak, Yunzhi Zhang +3
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, ou…
NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space
Jiawei Yao, Chuming Li, Keqiang Sun +4
Monocular 3D Semantic Scene Completion (SSC) has garnered significant attention in recent years due to its potential to predict complex semantics and geometry shapes from a single…
JourneyDB: A Benchmark for Generative Image Understanding
Keqiang Sun, Junting Pan, Yuying Ge +11
While recent advancements in vision-language models have had a transformative impact on multi-modal comprehension, the extent to which these models possess the ability to comprehen…
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Xiaoshi Wu, Yiming Hao, Keqiang Sun +4
Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing eval…
Human Preference Score: Better Aligning Text-to-Image Models with Human Preference
Xiaoshi Wu, Keqiang Sun, Feng Zhu +2
Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often genera…
CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
Keqiang Sun, Shangzhe Wu, Ning Zhang +3
Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of con…
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
Keqiang Sun, Shangzhe Wu, Zhaoyang Huang +3
Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of con…
Inverting Generative Adversarial Renderer for Face Reconstruction
Jingtan Piao, Keqiang Sun, KwanYee Lin +2
Given a monocular face image as input, 3D face geometry reconstruction aims to recover a corresponding 3D face mesh. Recently, both optimization-based and learning-based face recon…
FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos
Keqiang Sun, Wayne Wu, Tinghao Liu +5
Recently, facial landmark detection algorithms have achieved remarkable performance on static images. However, these algorithms are neither accurate nor stable in motion-blurred vi…