8 citations · 28 across the 15 of their papers we have counts for
15 papers
EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds
Lu Chen, Yizhou Wang, Shixiang Tang +6
Learning an agent model that behaves like humans-capable of jointly perceiving the environment, predicting the future, and taking actions from a first-person perspective-is a funda…
Depth Any Video with Scalable Synthetic Data
Honghui Yang, Di Huang, Wei Yin +6
Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introd…
NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction
Yifan Wang, Di Huang, Weicai Ye +3
Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction. Although promising, SDF-based methods often fail to captu…
TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentation
Xiaopei Wu, Yuenan Hou, Xiaoshui Huang +8
Training deep models for LiDAR semantic segmentation is challenging due to the inherent sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity p…
PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines
ZiDong Wang, Zeyu Lu, Di Huang +4
In this paper, we introduce PredBench, a benchmark tailored for the holistic evaluation of spatio-temporal prediction networks. Despite significant progress in this field, there re…
Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D Space
Yuan Wang, Zhao Wang, Junhao Gong +8
In this paper, we introduce a novel path to human motion generation by focusing on 2D space. Traditional methods have primarily generated human motions in 3D, wh…
MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers
Yiwen Chen, Tong He, Di Huang +9
Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this poten…
Agent3D-Zero: An Agent for Zero-shot 3D Understanding
Sha Zhang, Di Huang, Jiajun Deng +4
The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence. The current common practice is to finetune Large Language Mod…
GVGEN: Text-to-3D Generation with Volumetric Representation
Xianglong He, Junyi Chen, Sida Peng +6
In recent years, 3D Gaussian splatting has emerged as a powerful technique for 3D reconstruction and generation, known for its fast and high-quality rendering capabilities. To addr…
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
Yixuan Wu, Yizhou Wang, Shixiang Tang +5
We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini. Our a…
Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
Haoyi Zhu, Yating Wang, Di Huang +3
In robot learning, the observation space is crucial due to the distinct characteristics of different modalities, which can potentially become a bottleneck alongside policy design.…
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Yizhou Wang, Yixuan Wu, Weizhen He +8
Human-centric perception tasks, e.g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports…
GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection
Yan Lu, Xinzhu Ma, Lei Yang +6
Geometry plays a significant role in monocular 3D object detection. It can be used to estimate object depth by using the perspective projection between object's physical size and 2…
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
Haoyi Zhu, Honghui Yang, Xiaoyang Wu +8
In contrast to numerous NLP and 2D vision foundational models, learning a 3D foundational model poses considerably greater challenges. This is primarily due to the inherent data va…
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
Honghui Yang, Sha Zhang, Di Huang +9
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown w…