1 citations · 1 across the 21 of their papers we have counts for
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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…