2 citations · 4 across the 12 of their papers we have counts for
12 papers
GSPotential: Camera Potential Field for Sparse-View 3D Gaussian Splatting
Zeyuan An, Yanghang Xiao, Zhiying Leng +2
3D Gaussian Splatting has achieved remarkable success in photorealistic rendering, yet it suffers from severe overfitting and geometric artifacts in sparse-view scenarios due to th…
HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
Boshui Chen, Zhaoxin Fan, Ke Wang +5
Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While recent hallucination detection…
Multimodal Priors-Augmented Text-Driven 3D Human-Object Interaction Generation
Yin Wang, Ziyao Zhang, Zhiying Leng +4
We address the challenging task of text-driven 3D human-object interaction (HOI) motion generation. Existing methods primarily rely on a direct text-to-HOI mapping, which suffers f…
Dynamic Worlds, Dynamic Humans: Generating Virtual Human-Scene Interaction Motion in Dynamic Scenes
Yin Wang, Zhiying Leng, Haitian Liu +3
Scenes are continuously undergoing dynamic changes in the real world. However, existing human-scene interaction generation methods typically treat the scene as static, which deviat…
Cross-Temporal 3D Gaussian Splatting for Sparse-View Guided Scene Update
Zeyuan An, Yanghang Xiao, Zhiying Leng +2
Maintaining consistent 3D scene representations over time is a significant challenge in computer vision. Updating 3D scenes from sparse-view observations is crucial for various rea…
Fine-grained text-driven dual-human motion generation via dynamic hierarchical interaction
Mu Li, Yin Wang, Zhiying Leng +3
Human interaction is inherently dynamic and hierarchical, where the dynamic refers to the motion changes with distance, and the hierarchy is from individual to inter-individual and…
MOST: Motion Diffusion Model for Rare Text via Temporal Clip Banzhaf Interaction
Yin Wang, Mu li, Zhiying Leng +2
We introduce MOST, a novel motion diffusion model via temporal clip Banzhaf interaction, aimed at addressing the persistent challenge of generating human motion from rare language…
Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation
Yin Wang, Mu Li, Jiapeng Liu +4
We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships spec…
HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generation
Zhiying Leng, Tolga Birdal, Xiaohui Liang +1
3D shape generation from text is a fundamental task in 3D representation learning. The text-shape pairs exhibit a hierarchical structure, where a general text like ``chair" covers…
D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction
Bowen Fu, Gu Wang, Chenyangguang Zhang +6
Reconstructing hand-held objects from a single RGB image is a challenging task in computer vision. In contrast to prior works that utilize deterministic modeling paradigms, we empl…
Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model
Yin Wang, Zhiying Leng, Frederick W. B. Li +2
Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise moti…
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
Zhiying Leng, Shun-Cheng Wu, Mahdi Saleh +6
Reconstructing both objects and hands in 3D from a single RGB image is complex. Existing methods rely on manually defined hand-object constraints in Euclidean space, leading to sub…