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20242026
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8 papers · 1 filter

cs.CV2025

NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos

Qingshan Xu, Jiao Liu, Shangshu Yu +6

In this paper, we aim to create physical digital twins of deformable objects under interaction. Existing methods focus more on the physical learning of current state modeling, but…

cs.CV2025

Streaming Drag-Oriented Interactive Video Manipulation: Drag Anything, Anytime!

Junbao Zhou, Yuan Zhou, Kesen Zhao +4

Achieving streaming, fine-grained control over the outputs of autoregressive video diffusion models remains challenging, making it difficult to ensure that they consistently align…

cs.LG2025

Generative Distribution Distillation

Jiequan Cui, Beier Zhu, Qingshan Xu +6

In this paper, we formulate the knowledge distillation (KD) as a conditional generative problem and propose the \textit{Generative Distribution Distillation (GenDD)} framework. A n…

cs.CV2025

DragNeXt: Rethinking Drag-Based Image Editing

Yuan Zhou, Junbao Zhou, Qingshan Xu +5

Drag-Based Image Editing (DBIE), which allows users to manipulate images by directly dragging objects within them, has recently attracted much attention from the community. However…

cs.CV2025

On Path to Multimodal Generalist: General-Level and General-Bench

Hao Fei, Yuan Zhou, Juncheng Li +29

The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…

cs.CV2025

Personalize Your Gaussian: Consistent 3D Scene Personalization from a Single Image

Yuxuan Wang, Xuanyu Yi, Qingshan Xu +3

Personalizing 3D scenes from a single reference image enables intuitive user-guided editing, which requires achieving both multi-view consistency across perspectives and referentia…

cs.LG2025

Generalized Kullback-Leibler Divergence Loss

Jiequan Cui, Beier Zhu, Qingshan Xu +5

In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss…

cs.CV2025

Nautilus: Locality-aware Autoencoder for Scalable Mesh Generation

Yuxuan Wang, Xuanyu Yi, Haohan Weng +6

Triangle meshes are fundamental to 3D applications, enabling efficient modification and rasterization while maintaining compatibility with standard rendering pipelines. However, cu…