133 citations · 167 across the 7 of their papers we have counts for
8 papers
Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
Hanwen Liang, Yuyang Yin, Dejia Xu +5
The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rel…
Comp4D: LLM-Guided Compositional 4D Scene Generation
Dejia Xu, Hanwen Liang, Neel P. Bhatt +4
Recent advancements in diffusion models for 2D and 3D content creation have sparked a surge of interest in generating 4D content. However, the scarcity of 3D scene datasets constra…
Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts
Wenyan Cong, Hanxue Liang, Peihao Wang +5
Cross-scene generalizable NeRF models, which can directly synthesize novel views of unseen scenes, have become a new spotlight of the NeRF field. Several existing attempts rely on…
Edge-MoE: Memory-Efficient Multi-Task Vision Transformer Architecture with Task-level Sparsity via Mixture-of-Experts
Rishov Sarkar, Hanxue Liang, Zhiwen Fan +2
Computer vision researchers are embracing two promising paradigms: Vision Transformers (ViTs) and Multi-task Learning (MTL), which both show great performance but are computation-i…
Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views
Hanxue Liang, Tianhao Wu, Param Hanji +4
Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured i…
Surf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity
Tianhao Wu, Hanxue Liang, Fangcheng Zhong +4
Implicit surface representations such as the signed distance function (SDF) have emerged as a promising approach for image-based surface reconstruction. However, existing optimizat…
MViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
Hanxue Liang, Zhiwen Fan, Rishov Sarkar +6
Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often lets those tasks learn better jointly. However, when deploying MTL onto those real-world s…
One Million Scenes for Autonomous Driving: ONCE Dataset
Jiageng Mao, Minzhe Niu, Chenhan Jiang +10
Current perception models in autonomous driving have become notorious for greatly relying on a mass of annotated data to cover unseen cases and address the long-tail problem. On th…