1 citations · 1 across the 15 of their papers we have counts for
17 papers
StreamErase: Towards Ultra-Fast Streaming Video Erasing
Zhuobai Dong, Xiaobing Tu, Xiantao Zhang +4
Current video object removal methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction. We present StreamErase, enab…
Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision
Long Cui, Xiaoqian Liu, Qi Qin +4
Existing image editing frameworks predominantly follow the training paradigm of text-to-image diffusion models. However, extending this paradigm to image editing highlights two inh…
STEP-OPD: Rethinking Output Targets and Internal Dynamics in On-Policy Distillation for Diffusion Models
Qingyan Wei, Guangzhao Li, Xiaobing Tu +5
On-policy distillation (OPD) has become an effective approach for consolidating multiple task-specialized image generation models into a single student. However, existing OPD metho…
EgoGenesis: Egocentric World-Action Modeling with Online Anchored Projective Memory and Action-3D RoPE
Zexuan Yan, Yuzhou Wu, Yue Ma +9
Egocentric video offers rich manipulation experience for embodied AI, yet collecting diverse egocentric data across scenes, objects, motions, and embodiments remains costly. We pre…
OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal
Qinming Zhou, Chenxi Sun, Deyang Kong +6
Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which are difficult to model, and the…
FastVMT: Eliminating Redundancy in Video Motion Transfer
Yue Ma, Zhikai Wang, Tianhao Ren +9
Video motion transfer aims to synthesize videos by generating visual content according to a text prompt while transferring the motion pattern observed in a reference video. Recent…
LESA: Learnable Stage-Aware Predictors for Diffusion Model Acceleration
Peiliang Cai, Jiacheng Liu, Haowen Xu +3
Diffusion models have achieved remarkable success in image and video generation tasks. However, the high computational demands of Diffusion Transformers (DiTs) pose a significant c…
Towards Seamless Interaction: Causal Turn-Level Modeling of Interactive 3D Conversational Head Dynamics
Junjie Chen, Fei Wang, Zhihao Huang +5
Human conversation involves continuous exchanges of speech and nonverbal cues such as head nods, gaze shifts, and facial expressions that convey attention and emotion. Modeling the…
A Survey on Cache Methods in Diffusion Models: Toward Efficient Multi-Modal Generation
Jiacheng Liu, Xinyu Wang, Yuqi Lin +10
Diffusion Models have become a cornerstone of modern generative AI for their exceptional generation quality and controllability. However, their inherent \textit{multi-step iteratio…
Generation then Reconstruction: Accelerating Masked Autoregressive Models via Two-Stage Sampling
Feihong Yan, Peiru Wang, Yao Zhu +4
Masked Autoregressive (MAR) models promise better efficiency in visual generation than autoregressive (AR) models for the ability of parallel generation, yet their acceleration pot…
Follow-Your-Emoji-Faster: Towards Efficient, Fine-Controllable, and Expressive Freestyle Portrait Animation
Yue Ma, Zexuan Yan, Hongyu Liu +11
We present Follow-Your-Emoji-Faster, an efficient diffusion-based framework for freestyle portrait animation driven by facial landmarks. The main challenges in this task are preser…
Forecast then Calibrate: Feature Caching as ODE for Efficient Diffusion Transformers
Shikang Zheng, Liang Feng, Xinyu Wang +8
Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation. To reduce their substantial computational costs, feature cachin…
HiCache: A Plug-in Scaled-Hermite Upgrade for Taylor-Style Cache-then-Forecast Diffusion Acceleration
Liang Feng, Shikang Zheng, Jiacheng Liu +8
Diffusion models have achieved remarkable success in content generation but often incur prohibitive computational costs due to iterative sampling. Recent feature caching methods ac…
EVCtrl: Efficient Control Adapter for Visual Generation
Zixiang Yang, Yue Ma, Yinhan Zhang +3
Visual generation includes both image and video generation, training probabilistic models to create coherent, diverse, and semantically faithful content from scratch. While early r…
Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control
Zeqian Long, Mingzhe Zheng, Kunyu Feng +6
While recent flow-based image editing models demonstrate general-purpose capabilities across diverse tasks, they often struggle to specialize in challenging scenarios -- particular…
SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware Skipping
Jiajun Li, Yue Ma, Xinyu Zhang +3
Recent studies on Visual Autoregressive (VAR) models have highlighted that high-frequency components, or later steps, in the generation process contribute disproportionately to inf…
Controllable Video Generation: A Survey
Yue Ma, Kunyu Feng, Zhongyuan Hu +19
With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video…