1 citations · 2 across the 16 of their papers we have counts for
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UniPart: Towards Zero-shot Language-Grounded 3D Part Segmentation for Embodied Interaction
Xinqiang Yu, Zekun qi, Jiawei He +6
Fine-grained robotic manipulation depends on understanding parts, not only whole objects. Existing 3D foundation models tend to be either generalized but object-aware, or part-awar…
ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
Yuyang Zhang, Wenyao Zhang, Zekun Qi +7
World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense mult…
MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models
Hanyang Yu, Haitao Lin, Jingbo Zhang +4
World Action Models (WAMs) present a promising paradigm for robotic control via video prediction. However, current WAMs suffer from fundamental spatial bottlenecks: standard text i…
Reasoning in Space via Grounding in the World
Yiming Chen, Zekun Qi, Wenyao Zhang +3
In this paper, we claim that 3D visual grounding is the cornerstone of spatial reasoning and introduce the Grounded-Spatial Reasoner (GS-Reasoner) to explore the effective spatial…
Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation
Wenyao Zhang, Hongsi Liu, Bohan Li +7
Current self-supervised monocular depth estimation (MDE) approaches encounter performance limitations due to insufficient semantic-spatial knowledge extraction. To address this cha…
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
Wenyao Zhang, Hongsi Liu, Zekun Qi +11
Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot ma…
DexVLG: Dexterous Vision-Language-Grasp Model at Scale
Jiawei He, Danshi Li, Xinqiang Yu +7
As large models gain traction, vision-language-action (VLA) systems are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection,…
OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models
Mengdi Jia, Zekun Qi, Shaochen Zhang +5
Spatial reasoning is a key aspect of cognitive psychology and remains a bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or impr…
Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation
Yunnan Wang, Ziqiang Li, Zequn Zhang +5
There has been exciting progress in generating images from natural language or layout conditions. However, these methods struggle to faithfully reproduce complex scenes due to the…
Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion
Bohan Li, Jiajun Deng, Wenyao Zhang +4
Camera-based 3D semantic scene completion (SSC) is pivotal for predicting complicated 3D layouts with limited 2D image observations. The existing mainstream solutions generally lev…
Closed-Loop Unsupervised Representation Disentanglement with -VAE Distillation and Diffusion Probabilistic Feedback
Xin Jin, Bohan Li, BAAO Xie +5
Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks. It currently has at least three unreso…