37 citations · 39 across the 18 of their papers we have counts for
21 papers
Semantic Steering for Controllable Generation: Tuning-Free Concept Erasure in Multimodal Diffusion Transformers
Qiao Li, Xiaomeng Fu, Yuanshu Zhao +3
Multimodal Diffusion Transformers (MM-DiTs) have demonstrated remarkable text-to-image generation performance, surpassing traditional U-Net-based diffusion models. Nevertheless, th…
Erase but Preserve: Controllable Removal of Copyrighted Animation Characters via Optimized Semantic Anchors
Qiao Li, Xiaomeng Fu, Wangjia Yu +4
The exceptional generation capabilities of text-to-image diffusion models have raised copyright concerns, particularly the unauthorized reproduction of animation characters. Existi…
Towards Memory-Efficient Autoregressive Video Generation via Instance-Specific Parametric Absorption
Xiaomeng Fu, Jia Li, Yiming Hu +5
Autoregressive (AR) streaming models have emerged as a powerful paradigm for long video generation. However, the linearly growing Key-Value (KV) cache poses a significant bottlenec…
Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing
Runze He, Yiji Cheng, Tiankai Hang +11
In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful executi…
Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image Generation
Wenchao Zhang, Jiahe Tian, Runze He +5
Recent text-to-image (T2I) generation models have advanced significantly, enabling the creation of high-fidelity images from textual prompts. However, existing evaluation benchmark…
Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation
Baisen Wang, Le Zhuo, Zhaokai Wang +7
Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fu…
FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction
Runze He, Kai Ma, Linjiang Huang +6
Introducing user-specified visual concepts in image editing is highly practical as these concepts convey the user's intent more precisely than text-based descriptions. We propose F…
Learning to Discover Forgery Cues for Face Forgery Detection
Jiahe Tian, Peng Chen, Cai Yu +4
Locating manipulation maps, i.e., pixel-level annotation of forgery cues, is crucial for providing interpretable detection results in face forgery detection. Related learning objec…
Unveiling Structural Memorization: Structural Membership Inference Attack for Text-to-Image Diffusion Models
Qiao Li, Xiaomeng Fu, Xi Wang +4
With the rapid advancements of large-scale text-to-image diffusion models, various practical applications have emerged, bringing significant convenience to society. However, model…
Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
Cai Yu, Shan Jia, Xiaomeng Fu +6
With the rising prevalence of deepfakes, there is a growing interest in developing generalizable detection methods for various types of deepfakes. While effective in their specific…
Model Will Tell: Training Membership Inference for Diffusion Models
Xiaomeng Fu, Xi Wang, Qiao Li +3
Diffusion models pose risks of privacy breaches and copyright disputes, primarily stemming from the potential utilization of unauthorized data during the training phase. The Traini…
Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training
Runze He, Shaofei Huang, Xuecheng Nie +6
In this paper, we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt…
Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding
Tianrui Hui, Zihan Ding, Junshi Huang +5
Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption. As a multimodal task, an essential aspect…
OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motions
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation has no explicit head movement reference, thus it is difficult to generate talking heads with head motions. Some existing works only edit the mouth…
Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation
Shaofei Huang, Han Li, Yuqing Wang +5
Audio visual segmentation (AVS) aims to segment the sounding objects for each frame of a given video. To distinguish the sounding objects from silent ones, both audio-visual semant…
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model
Jin Liu, Xi Wang, Xiaomeng Fu +4
Face-to-face communication is a common scenario including roles of speakers and listeners. Most existing research methods focus on producing speaker videos, while the generation of…
A Unified Framework for Modality-Agnostic Deepfakes Detection
Cai Yu, Peng Chen, Jiahe Tian +7
As AI-generated content (AIGC) thrives, deepfakes have expanded from single-modality falsification to cross-modal fake content creation, where either audio or visual components can…
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation has received growing attention in recent years, with various creative and practical applications. An ideal natural and vivid generated talking head…
OPT: One-shot Pose-Controllable Talking Head Generation
Jin Liu, Xi Wang, Xiaomeng Fu +4
One-shot talking head generation produces lip-sync talking heads based on arbitrary audio and one source face. To guarantee the naturalness and realness, recent methods propose to…
Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection
Shaofei Huang, Zhenwei Shen, Zehao Huang +5
Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the b…
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network
Jin Liu, Peng Chen, Tao Liang +5
Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously. Most existing methods directly apply driving facial la…