1 citations · 3 across the 49 of their papers we have counts for
39 papers · 1 filter
Towards Practical 20 kbps Generative Video Compression via Adaptive Tokenization and Entropy Modeling
Jiancheng Zhao, Jixiang Luo, Haibin Huang +2
Video compression at ultra-low bitrates remains challenging due to the loss of perceptually important details under limited bit budgets. Recent generative video compression methods…
Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation
Zixuan Duan, Xunzhi Xiang, Yabo Chen +6
Few-step distillation improves the efficiency of autoregressive (AR) video generation, but often causes diversity collapse: under the same prompt, different noise samples tend to p…
Search-to-World: Evaluation of 3D World Delivery from User Request through Web Search
Zixiao Gu, Yabo Chen, Xunzhi Xiang +5
Agentic systems can interpret user requests, search the live web, and use external tools, but their ability to transform retrieved web content into a usable 3D world has not been s…
TourPhysics: Bringing Physics to World Models for Exploration and Manipulation from a Single Image
Xin Zhang, Yabo Chen, Zixuan Duan +4
Interactive visual world models must distinguish observation from physical intervention. Camera motion reveals new surfaces, whereas intervention changes object motion, contact, an…
RefVideo-6M: A Reliable Reference-Based Dataset for Instructional Video Editing
Bojia Zi, Xiaoyan Yang, Yu Zhou +7
Recent advances in video editing have been largely driven by large-scale instruction-based datasets. However, existing datasets still suffer from two critical limitations. First, t…
Towards Expressive and Faithful Audio-to-Image Generation: A Unified Multimodal Dataset and Synthesis Framework
Dongxu Ge, Shansong Liu, Cheng Gong +3
As an important subfield of cross-modal generation, synthesizing static visual content in the form of images from audio, namely audio-to-image (A2I) generation, has attracted incre…
CineWeaver: Training-Free Reference-Controllable Multi-Shot Long Video Generation for Cinematic Storytelling
Yuyang Huang, Yabo Chen, Wenrui Dai +6
Cinematic video generation is challenging for text-to-video diffusion models due to concurrent requirements on multi-shot generation, fine-grained controllability over characters a…
ShotPlan: Cinematic Video Generation with Learnable Planning Token
Su Guo, Guangce Liu, Haosen Yang +7
Current video generation models achieve impressive results in single-shot generation, yet remain limited in cinematic video generation, where coherent narratives and effective mult…
Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning
Shiwen Zhang, Haoyuan Wang, Xianghao Zang +3
Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to entangled content and style features. With a rev…
InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars
Quanyue Song, Yishan He, Yanfei Zhang +6
Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing approaches struggle to maintain visual temporal consis…
Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation
Shihao Cheng, Jiaxu Zhang, Quanyue Song +6
Motion, speech, and sound effects are fundamental elements of human-centric videos, yet their heterogeneous temporal characteristics make joint generation highly challenging. Exist…
Directing the World: Fast Autoregressive Video Generation with Compositional Human-Camera Control
Haoyuan Wang, Yabo Chen, Haibin Huang +2
Building interactive world models requires generating realistic videos while maintaining controllable dynamics over long horizons. Autoregressive video generation offers a scalable…
TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillation
Shiwen Zhang, Yifan Xu, Haibin Huang +2
Given a content reference and a style reference, content-preserving style transfer requires the model to generate stylized outputs with content and style consistency. We introduced…
VideoWeave: Unlocking Geometric Consistency in Video Generation via Joint Geometry-Video Modeling
Xunzhi Xiang, Zixuan Duan, Yabo Chen +8
Large-scale video diffusion models often fail to preserve 3D structure over time, causing geometric drift and implausible motion under viewpoint changes. Existing methods usually e…
SIFT: Self-Imagination Fine-Tuning for Physically Plausible Motion in Video Diffusion Models
Ruoyu Wang, Jialun Liu, Huayang Huang +5
Recent advances in video diffusion models have greatly improved visual fidelity, yet their generated motions often violate physical plausibility. We observe a common kinematic fail…
Full-4D: Generating Full-Scope 4D Scenes from a Single-View Video
Tingxi Chen, Ke Hao, Yabo Chen +6
Generating 4D scenes from a single-view video is inherently ill-posed: a single viewpoint lacks the information needed to recover a complete, dynamic scene with full coverage. Exis…
Smart-Insertion-V: Photorealistic Video Insertion via a Closed-Loop Feedback Dual-Stream Framework
Xiao Cao, Yansong Qu, Xiangzhen +8
Mask-free video object insertion has emerged as a challenging task, requiring harmonious integration of reference objects into source videos. However, existing methods struggle whe…
Q-GeoMem: Question-Guided Geometric Memory for Video Spatial Reasoning
Xianqiang Gao, Qizhi Chen, Delin Qu +5
Video spatial reasoning requires accumulating viewpoint-dependent evidence over time while retaining information useful to the question being asked. Existing spatial video-language…
VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing
Yanbin Huang, Yisen Li, Guiyao Tie +6
Large vision-language models (LVLMs) frequently suffer from Object Hallucination (OH), wherein they generate descriptions containing objects that are not actually present in the in…
Multi-Domain Learning with Global Expert Mapping
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +3
Human perception generalizes well across different domains, but most vision models struggle beyond their training data. This gap motivates multi-dataset learning, where a single mo…
Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation
Rui Li, Ke Hao, Yuanzhi Liang +4
Reinforcement learning, particularly Group Relative Policy Optimization (GRPO), has emerged as an effective framework for post-training visual generative models with human preferen…
Point2Insert: Video Object Insertion via Sparse Point Guidance
Yu Zhou, Xiaoyan Yang, Bojia Zi +6
This paper introduces Point2Insert, a sparse-point-based framework for flexible and user-friendly object insertion in videos, motivated by the growing popularity of accurate, low-e…
TeleStyle: Content-Preserving Style Transfer in Images and Videos
Shiwen Zhang, Xiaoyan Yang, Bojia Zi +3
Content-preserving style transfer, generating stylized outputs based on content and style references, remains a significant challenge for Diffusion Transformers (DiTs) due to the i…
QwenStyle: Content-Preserving Style Transfer with Qwen-Image-Edit
Shiwen Zhang, Haibin Huang, Chi Zhang +1
Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to its internal entangled content and style feature…