Yiming Wang

Yiming Wang, Lucy Chai, Xuan Luo et al.

We study the problem of novel view streaming from sparse-view videos, which aims to generate a continuous sequence of high-quality, temporally consistent novel views as new input frames arrive. However, existing novel view synthesis methods struggle with temporal coherence and visual fidelity, leading to flickering and inconsistency. To address these challenges, we introduce history-awareness, leveraging previous frames to reconstruct the scene and improve quality and stability. We propose a hybrid splat-voxel feed-forward scene reconstruction approach that combines Gaussian Splatting to propagate information over time, with a hierarchical voxel grid for temporal fusion. Gaussian primitives are efficiently warped over time using a motion graph that extends 2D tracking models to 3D motion, while a sparse voxel transformer integrates new temporal observations in an error-aware manner. Crucially, our method does not require training on multi-view video datasets, which are currently limited in size and diversity, and can be directly applied to sparse-view video streams in a history-aware manner at inference time. Our approach achieves state-of-the-art performance in both static and streaming scene reconstruction, effectively reducing temporal artifacts and visual artifacts while running at interactive rates (15 fps with 350ms delay) on a single H100 GPU. Project Page: https://19reborn.github.io/SplatVoxel/

Jiahao Chen, junhao li, Yiming Wang et al.

The proliferation of Low-Rank Adaptation (LoRA) models has democratized personalized text-to-image generation, enabling users to share lightweight models (e.g., personal portraits) on platforms like Civitai and Liblib. However, this "share-and-play" ecosystem introduces critical risks: benign LoRAs can be weaponized by adversaries to generate harmful content (e.g., political, defamatory imagery), undermining creator rights and platform safety. Existing defenses like concept-erasure methods focus on full diffusion models (DMs), neglecting LoRA's unique role as a modular adapter and its vulnerability to adversarial prompt engineering. To bridge this gap, we propose LoRAShield, the first data-free editing framework for securing LoRA models against misuse. Our platform-driven approach dynamically edits and realigns LoRA's weight subspace via adversarial optimization and semantic augmentation. Experimental results demonstrate that LoRAShield achieves remarkable effectiveness, efficiency, and robustness in blocking malicious generations without sacrificing the functionality of the benign task. By shifting the defense to platforms, LoRAShield enables secure, scalable sharing of personalized models, a critical step toward trustworthy generative ecosystems.

Yiming Wang, Dongxia Chang, Zhiqiang Fu et al.

In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and recovers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.