most citedControllable Video Generation: A Survey

1 citations · 1 across the 15 of their papers we have counts for

collaborators
Showing cs.CVShow all

14 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…