2 citations · 5 across the 12 of their papers we have counts for
14 papers · 1 filter
LibraGen: Playing a Balance Game in Subject-Driven Video Generation
Jiahao Zhu, Shanshan Lao, Lijie Liu +10
With the advancement of video generation foundation models (VGFMs), customized generation, particularly subject-to-video (S2V), has attracted growing attention. However, a key chal…
DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation
Xu Guo, Fulong Ye, Qichao Sun +7
Recent advancements in foundation models have revolutionized joint audio-video generation. However, existing approaches typically treat human-centric tasks including reference-base…
OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models
Jinshu Chen, Xinghui Li, Xu Bai +8
Recent advances in video insertion based on diffusion models are impressive. However, existing methods rely on complex control signals but struggle with subject consistency, limiti…
HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning
Liyang Chen, Tianxiang Ma, Jiawei Liu +7
Human-Centric Video Generation (HCVG) methods seek to synthesize human videos from multimodal inputs, including text, image, and audio. Existing methods struggle to effectively coo…
Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset
Zhuowei Chen, Bingchuan Li, Tianxiang Ma +8
Subject-to-video generation has witnessed substantial progress in recent years. However, existing models still face significant challenges in faithfully following textual instructi…
HyperLoRA: Parameter-Efficient Adaptive Generation for Portrait Synthesis
Mengtian Li, Jinshu Chen, Wanquan Feng +4
Personalized portrait synthesis, essential in domains like social entertainment, has recently made significant progress. Person-wise fine-tuning based methods, such as LoRA and Dre…
Phantom: Subject-consistent video generation via cross-modal alignment
Lijie Liu, Tianxiang Ma, Bingchuan Li +6
The continuous development of foundational models for video generation is evolving into various applications, with subject-consistent video generation still in the exploratory stag…
Customize Your Own Paired Data via Few-shot Way
Jinshu Chen, Bingchuan Li, Miao Hua +2
Existing solutions to image editing tasks suffer from several issues. Though achieving remarkably satisfying generated results, some supervised methods require huge amounts of pair…
GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images
Tianxiang Ma, Bingchuan Li, Qian He +2
While current face animation methods can manipulate expressions individually, they suffer from several limitations. The expressions manipulated by some motion-based facial reenactm…
CFFT-GAN: Cross-domain Feature Fusion Transformer for Exemplar-based Image Translation
Tianxiang Ma, Bingchuan Li, Wei Liu +3
Exemplar-based image translation refers to the task of generating images with the desired style, while conditioning on certain input image. Most of the current methods learn the co…
Semantic 3D-aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field
Tianxiang Ma, Bingchuan Li, Qian He +2
Recently 3D-aware GAN methods with neural radiance field have developed rapidly. However, current methods model the whole image as an overall neural radiance field, which limits th…
ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing
Bingchuan Li, Tianxiang Ma, Peng Zhang +4
The StyleGAN family succeed in high-fidelity image generation and allow for flexible and plausible editing of generated images by manipulating the semantic-rich latent style space.…
FaceEraser: Removing Facial Parts for Augmented Reality
Miao Hua, Lijie Liu, Ziyang Cheng +3
Our task is to remove all facial parts (e.g., eyebrows, eyes, mouth and nose), and then impose visual elements onto the ``blank'' face for augmented reality. Conventional object re…
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
Bingchuan Li, Shaofei Cai, Wei Liu +4
The semantic controllability of StyleGAN is enhanced by unremitting research. Although the existing weak supervision methods work well in manipulating the style codes along one att…