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TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control
Wei-Jin Huang, Yuan-Ming Li, Kun-Yu Lin +8
Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment slow and memory-intensive. W…
TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes
Jun-Hui Liu, Kun-Yu Lin, Yi-Lin Wei +9
This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate attacks through stealthy textual…
Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation
Junjin Xiao, Dongyang Li, Yandan Yang +9
This paper tackles spatial perception and manipulation challenges in Vision-Language-Action (VLA) models. To address depth ambiguity from monocular input, we leverage a pre-trained…
Generative Texture Filtering
Rongjia Zheng, Shangwei Huang, Lei Zhu +2
We present a generative method for texture filtering, which exhibits surprisingly good performance and generalizability. Our core idea is to empower texture filtering by taking ful…
SGS-Intrinsic: Semantic-Invariant Gaussian Splatting for Sparse-View Indoor Inverse Rendering
Jiahao Niu, Rongjia Zheng, Wenju Xu +2
We present SGS-Intrinsic, an indoor inverse rendering framework that works well for sparse-view images. Unlike existing 3D Gaussian Splatting (3DGS) based methods that focus on obj…
You Only Erase Once: Erasing Anything without Bringing Unexpected Content
Yixing Zhu, Qing Zhang, Wenju Xu +1
We present YOEO, an approach for object erasure. Unlike recent diffusion-based methods which struggle to erase target objects without generating unexpected content within the maske…
Learning Explicit Continuous Motion Representation for Dynamic Gaussian Splatting from Monocular Videos
Xuankai Zhang, Junjin Xiao, Shangwei Huang +2
We present an approach for high-quality dynamic Gaussian Splatting from monocular videos. To this end, we in this work go one step further beyond previous methods to explicitly mod…