110 citations · 243 across the 40 of their papers we have counts for
43 papers · 1 filter
From Pixels to Actions--Towards Native Vision-Language-Action Policies
Weichen Fan, Haiwen Diao, Penghao Wu +14
Vision-language-action models are typically built on a simple premise: perception is first compressed by visual encoders, and action reasoning begins only afterwards. Yet manipulat…
EgoTools: Towards Tool-Centric Reasoning in Real-World Egocentric Videos
Shulin Tian, Junsu Kim, Shuai Liu +17
Real-world embodied tasks, from everyday activities to professional procedures, require agents to act under physical constraints while tracking evolving object and task states. Too…
S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence
Yalun Dai, Hao Li, Shulin Tian +8
Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inf…
IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer
Zhengyu Zou, Hao Li, Kuixuan Jiao +7
Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spa…
SpatialBench: Is Your Spatial Foundation Model an All-Round Player?
Haosong Peng, Hao Li, Jiaqi Chen +10
While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing…
PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image
Ziang Cao, Fangzhou Hong, Zhaoxi Chen +2
3D modeling is shifting from static visual representations toward physical, articulated assets that can be directly used in simulation and interaction. However, most existing 3D ge…
IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction
Hao Li, Zhengyu Zou, Fangfu Liu +8
Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions, enabling coherent and accurate understanding of complex scenes. Howe…
4DNeX: Feed-Forward 4D Generative Modeling Made Easy
Zhaoxi Chen, Tianqi Liu, Long Zhuo +6
We present 4DNeX, the first feed-forward framework for generating 4D (i.e., dynamic 3D) scene representations from a single image. In contrast to existing methods that rely on comp…
EgoTwin: Dreaming Body and View in First Person
Jingqiao Xiu, Fangzhou Hong, Yicong Li +5
While exocentric video synthesis has achieved great progress, egocentric video generation remains largely underexplored, which requires modeling first-person view content along wit…
Reconstructing 4D Spatial Intelligence: A Survey
Yukang Cao, Jiahao Lu, Zhisheng Huang +8
Reconstructing 4D spatial intelligence from visual observations has long been a central yet challenging task in computer vision, with broad real-world applications. These range fro…
3D Scene Generation: A Survey
Haozhe Xie, Beichen Wen, Zhaoxi Chen +2
3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as immersive media, robotics, autonomou…
Compositional Generative Model of Unbounded 4D Cities
Haozhe Xie, Zhaoxi Chen, Fangzhou Hong +1
3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of…
GeneMAN: Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data
Wentao Wang, Hang Ye, Fangzhou Hong +5
Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model. Existing methods face difficulties including a) the varying bod…
3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong +10
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative mo…
Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild
Lingni Ma, Yuting Ye, Fangzhou Hong +14
We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) fu…
Generative Gaussian Splatting for Unbounded 3D City Generation
Haozhe Xie, Zhaoxi Chen, Fangzhou Hong +1
3D city generation with NeRF-based methods shows promising generation results but is computationally inefficient. Recently 3D Gaussian Splatting (3D-GS) has emerged as a highly eff…
FashionEngine: Interactive 3D Human Generation and Editing via Multimodal Controls
Tao Hu, Fangzhou Hong, Zhaoxi Chen +1
We present FashionEngine, an interactive 3D human generation and editing system that creates 3D digital humans via user-friendly multimodal controls such as natural languages, visu…
4D Panoptic Scene Graph Generation
Jingkang Yang, Jun Cen, Wenxuan Peng +6
We are living in a three-dimensional space while moving forward through a fourth dimension: time. To allow artificial intelligence to develop a comprehensive understanding of such…
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D Generation
Ziang Cao, Fangzhou Hong, Tong Wu +2
Generating diverse and high-quality 3D assets automatically poses a fundamental yet challenging task in 3D computer vision. Despite extensive efforts in 3D generation, existing opt…
SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering
Tao Hu, Fangzhou Hong, Ziwei Liu
Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However, existing methods…
Large Motion Model for Unified Multi-Modal Motion Generation
Mingyuan Zhang, Daisheng Jin, Chenyang Gu +8
Human motion generation, a cornerstone technique in animation and video production, has widespread applications in various tasks like text-to-motion and music-to-dance. Previous wo…
StructLDM: Structured Latent Diffusion for 3D Human Generation
Tao Hu, Fangzhou Hong, Ziwei Liu
Recent 3D human generative models have achieved remarkable progress by learning 3D-aware GANs from 2D images. However, existing 3D human generative methods model humans in a compac…
LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
Yushi Lan, Fangzhou Hong, Shangchen Zhou +7
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved su…
3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors
Fangzhou Hong, Jiaxiang Tang, Ziang Cao +8
We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage…