14 citations · 72 across the 22 of their papers we have counts for
22 papers
SE-Search: Self-Evolving Search Agent via Memory and Dense Reward
Jian Li, Yizhang Jin, Dongqi Liu +9
Retrieval augmented generation (RAG) reduces hallucinations and factual errors in large language models (LLMs) by conditioning generation on retrieved external knowledge. Recent se…
DiP: Taming Diffusion Models in Pixel Space
Zhennan Chen, Junwei Zhu, Xu Chen +6
Diffusion models face a fundamental trade-off between generation quality and computational efficiency. Latent Diffusion Models (LDMs) offer an efficient solution but suffer from po…
FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning
Bo Yin, Xiaobin Hu, Xingyu Zhou +7
Diffusion models have achieved remarkable success in generative modeling, yet how to effectively adapt large pretrained models to new tasks remains challenging. We revisit the reco…
SVFR: A Unified Framework for Generalized Video Face Restoration
Zhiyao Wang, Xu Chen, Chengming Xu +7
Face Restoration (FR) is a crucial area within image and video processing, focusing on reconstructing high-quality portraits from degraded inputs. Despite advancements in image FR,…
HiFiVFS: High Fidelity Video Face Swapping
Xu Chen, Keke He, Junwei Zhu +3
Face swapping aims to generate results that combine the identity from the source with attributes from the target. Existing methods primarily focus on image-based face swapping. Whe…
SVP: Style-Enhanced Vivid Portrait Talking Head Diffusion Model
Weipeng Tan, Chuming Lin, Chengming Xu +5
Talking Head Generation (THG), typically driven by audio, is an important and challenging task with broad application prospects in various fields such as digital humans, film produ…
RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D Facial Prior-guided Identity Alignment Network
Xiaozhong Ji, Chuming Lin, Zhonggan Ding +6
Person-generic audio-driven face generation is a challenging task in computer vision. Previous methods have achieved remarkable progress in audio-visual synchronization, but there…
Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control
Yue Han, Junwei Zhu, Keke He +7
Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities…
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability
Xuelin Qian, Yu Wang, Simian Luo +9
Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. In this paper, we extend auto-regressive models to 3D…
A Generalist FaceX via Learning Unified Facial Representation
Yue Han, Jiangning Zhang, Junwei Zhu +7
This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously. To achieve this goal, we initially formulate a unified…
Dynamic Frame Interpolation in Wavelet Domain
Lingtong Kong, Boyuan Jiang, Donghao Luo +4
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by em…
High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning
Chao Xu, Junwei Zhu, Jiangning Zhang +6
Recently, emotional talking face generation has received considerable attention. However, existing methods only adopt one-hot coding, image, or audio as emotion conditions, thus la…
Learning Versatile 3D Shape Generation with Improved AR Models
Simian Luo, Xuelin Qian, Yanwei Fu +5
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the…
Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
Yifeng Zhou, Chuming Lin, Donghao Luo +4
To achieve promising results on blind image super-resolution (SR), some attempts leveraged the low resolution (LR) images to predict the kernel and improve the SR performance. Howe…
SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer
Haoran Zhou, Yun Cao, Wenqing Chu +4
Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of…
IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
Lingtong Kong, Boyuan Jiang, Donghao Luo +5
Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters…
High-resolution Iterative Feedback Network for Camouflaged Object Detection
Xiaobin Hu, Shuo Wang, Xuebin Qin +5
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the…
CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning
Yue Wang, Ran Yi, Luying Li +3
Generating artistic portraits is a challenging problem in computer vision. Existing portrait stylization models that generate good quality results are based on Image-to-Image Trans…
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation
Yifeng Chen, Wenqing Chu, Fangfang Wang +6
Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently.…
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
Yuhan Wang, Xu Chen, Junwei Zhu +7
In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unli…
Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection
Zhenyu Zhang, Yanhao Ge, Renwang Chen +6
Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on…
Context-Aware Image Inpainting with Learned Semantic Priors
Wendong Zhang, Junwei Zhu, Ying Tai +5
Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still cha…