8 citations · 27 across the 15 of their papers we have counts for
16 papers
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild
Weicai Ye, Xinyu Chen, Ruohao Zhan +7
This paper proposes a concise, elegant, and robust pipeline to estimate smooth camera trajectories and obtain dense point clouds for casual videos in the wild. Traditional framewor…
Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction
Junyi Chen, Di Huang, Weicai Ye +2
Spatial intelligence is the ability of a machine to perceive, reason, and act in three dimensions within space and time. Recent advancements in large-scale auto-regressive models h…
EMMA: End-to-End Multimodal Model for Autonomous Driving
Jyh-Jing Hwang, Runsheng Xu, Hubert Lin +11
We introduce EMMA, an End-to-end Multimodal Model for Autonomous driving. Built upon a multi-modal large language model foundation like Gemini, EMMA directly maps raw camera sensor…
MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding
Yuan Wang, Di Huang, Yaqi Zhang +5
Generating lifelike human motions from descriptive texts has experienced remarkable research focus in the recent years, propelled by the emerging requirements of digital humans.Des…
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…
GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction
Junyi Chen, Weicai Ye, Yifan Wang +6
3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. Previous methods adapt it to obtaining surfaces of either individual 3D objects or within limi…
ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction
Ziyu Tang, Weicai Ye, Yifan Wang +4
Neural implicit reconstruction via volume rendering has demonstrated its effectiveness in recovering dense 3D surfaces. However, it is non-trivial to simultaneously recover meticul…
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…
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…
NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection
Chenxi Huang, Yuenan Hou, Weicai Ye +5
NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by innovatively utilizing NeRF to enhance representation learning. Despite its notable performance, w…
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.…
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…