1 citations · 1 across the 21 of their papers we have counts for
24 papers
VideoX-Qwen: Data-Centric Instruction-Based Video Editing
JJiahang Li, Dingbao Shao, Xinyu Chen +8
Progress in general-purpose video editing depends on constructing large-scale paired supervision and effectively adapting video-generation backbones to instruction-driven editing.…
Linguistic Trajectory Encoding for Efficient Long-Horizon Spatial Memory in Embodied Agents
Tianyidan Xie, Shenyi Wang, Qiang Tang +7
Embodied agents performing long-horizon tasks require a memory representation in which the state transitions of dynamic objects remain queryable in natural language across hours-to…
InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors
Dingbao Shao, Song Wu, Xinyu Chen +18
Video virtual try-on is a highly constrained editing task requiring the precise replacement of a target person's clothing while strictly preserving the original video's spatial str…
EgoMonth: A Month-Level Egocentric Video Benchmark for Long-Term Spatiotemporal Memory
Weitao Chen, Hu Jiaxin, Xie Tianyidan +15
Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number of long video benchmarks. Howev…
SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model
Zhennan Chen, Tianxing Shi, Pengcheng Xu +5
VAR has gained widespread popularity due to its next-scale prediction paradigm. However, it faces substantial performance bottlenecks when handling complex scenes with multiple obj…
SimInsert: Seamless Video Object Insertion via Regional Sparse Attention Fusion
Xinyu Chen, Yuyi Qian, Jiang Lin +9
Video object insertion requires ensuring spatio-temporal coherence and interactive realism, extending far beyond simple content placement. However, current approaches are often hin…
Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance
Song Wu, Xinyu Chen, Qian Wang +3
Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data collection and model training, they often underutilize the…
TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On
Dingbao Shao, Song Wu, Shenyi Wang +9
Due to the scarcity of large-scale in-the-wild triplet data and the improper use of masks, the performance of video virtual try-on models remains limited. In this paper, we first i…
PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement
Tianyidan Xie, Peiyu Wang, Yuyi Qian +11
Translating natural-language descriptions of physical phenomena into executable simulation code requires both programming expertise and physical reasoning. Current large language m…
CineAGI: Character-Consistent Movie Creation through LLM-Orchestrated Multi-Modal Generation and Cross-Scene Integration
Tianyidan Xie, Zhentao Huang, Mingjie Wang +4
Automated movie creation requires coordinating multiple characters, modalities, and narrative elements across extended sequences -- a challenge that existing end-to-end approaches…
PhysLayer: Language-Guided Layered Animation with Depth-Aware Physics
Tianyidan Xie, Zhentao Huang, Mingjie Wang +4
Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior approaches have incorporated phys…
A training-free framework for high-fidelity appearance transfer via diffusion transformers
Shengrong Gu, Ye Wang, Song Wu +4
Diffusion Transformers (DiTs) excel at generation, but their global self-attention makes controllable, reference-image-based editing a distinct challenge. Unlike U-Nets, naively in…
PairHuman: A High-Fidelity Photographic Dataset for Customized Dual-Person Generation
Ting Pan, Ye Wang, Peiguang Jing +3
Personalized dual-person portrait customization has considerable potential applications, such as preserving emotional memories and facilitating wedding photography planning. Howeve…
FreeControl: Efficient, Training-Free Structural Control via One-Step Attention Extraction
Jiang Lin, Xinyu Chen, Song Wu +7
Controlling the spatial and semantic structure of diffusion-generated images remains a challenge. Existing methods like ControlNet rely on handcrafted condition maps and retraining…
OmniStyle2: Learning to Stylize by Learning to Destylize
Ye Wang, Zili Yi, Yibo Zhang +6
This paper introduces a scalable paradigm for supervised style transfer by inverting the problem: instead of learning to stylize directly, we learn to destylize, reducing stylistic…
OmniStyle: Filtering High Quality Style Transfer Data at Scale
Ye Wang, Ruiqi Liu, Jiang Lin +4
In this paper, we introduce OmniStyle-1M, a large-scale paired style transfer dataset comprising over one million content-style-stylized image triplets across 1,000 diverse style c…
From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective
Chen Zhao, Zhizhou Chen, Yunzhe Xu +6
Ultra-high-definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we ana…
Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging
Jiang Lin, Zili Yi
Video style transfer aims to alter the style of a video while preserving its content. Previous methods often struggle with content leakage and style misalignment, particularly when…
DP-Adapter: Dual-Pathway Adapter for Boosting Fidelity and Text Consistency in Customizable Human Image Generation
Ye Wang, Xuping Xie, Lanjun Wang +2
With the growing popularity of personalized human content creation and sharing, there is a rising demand for advanced techniques in customized human image generation. However, curr…
SigStyle: Signature Style Transfer via Personalized Text-to-Image Models
Ye Wang, Tongyuan Bai, Xuping Xie +3
Style transfer enables the seamless integration of artistic styles from a style image into a content image, resulting in visually striking and aesthetically enriched outputs. Despi…
One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
Dongqi Fan, Tao Chen, Mingjie Wang +5
Current Pose-Guided Person Image Synthesis (PGPIS) methods depend heavily on large amounts of labeled triplet data to train the generator in a supervised manner. However, they ofte…
Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation
Tianyidan Xie, Rui Ma, Qian Wang +6
Recent advancements in image-conditioned image generation have demonstrated substantial progress. However, foreground-conditioned image generation remains underexplored, encounteri…
OSTAF: A One-Shot Tuning Method for Improved Attribute-Focused T2I Personalization
Ye Wang, Zili Yi, Rui Ma
Personalized text-to-image (T2I) models not only produce lifelike and varied visuals but also allow users to tailor the images to fit their personal taste. These personalization te…
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation
Peng Zheng, Tao Liu, Zili Yi +1
With the development of neural radiance fields and generative models, numerous methods have been proposed for learning 3D human generation from 2D images. These methods allow contr…