1 citations · 3 across the 14 of their papers we have counts for
17 papers
OneLA: Learning Many Worlds from Latent Actions
Ziyu Zhu, Zhaoxu Sun, Fan Jiang +3
Learning world models across diverse domains calls for a unified control space, yet ac- tion interfaces vary across domains and action annotations are often scarce. Latent ac- tion…
GAD: Geometry Attention Distillation for Consistent Video Generation
Junfeng Ni, Ziyu Zhu, Fan Jiang +7
Video diffusion models generate visually compelling short-term sequences but often struggle to preserve scene geometry and object structure over time. In this paper, we first demon…
LongForcing: Scaling Temporal Manifold Matching for Long-Horizon World Rollout
Ziyu Zhu, Zhaoxu Sun, Fan Jiang +5
Autoregressive video diffusion models have emerged as a foundation for long-horizon world rollout. However, when extended far beyond their training windows, these models suffer fro…
ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU
Fan Jiang, Zhaoxu Sun, Mengchao Wang +38
We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA g…
Lifting Unlabeled Internet-level Data for 3D Scene Understanding
Yixin Chen, Yaowei Zhang, Huangyue Yu +9
Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this paper, we demonstrate that carefully desi…
Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields
Shiqian Li, Ruihong Shen, Junfeng Ni +3
Predicting physical dynamics from raw visual data remains a major challenge in AI. While recent video generation models have achieved impressive visual quality, they still cannot c…
3D Scene Change Modeling With Consistent Multi-View Aggregation
Zirui Zhou, Junfeng Ni, Shujie Zhang +2
Change detection plays a vital role in scene monitoring, exploration, and continual reconstruction. Existing 3D change detection methods often exhibit spatial inconsistency in the…
G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior
Junfeng Ni, Yixin Chen, Zhifei Yang +4
Despite recent advances in leveraging generative prior from pre-trained diffusion models for 3D scene reconstruction, existing methods still face two critical limitations. First, d…
VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video
Yu Liu, Baoxiong Jia, Ruijie Lu +5
Building digital twins of articulated objects from monocular video presents an essential challenge in computer vision, which requires simultaneous reconstruction of object geometry…
Trace3D: Consistent Segmentation Lifting via Gaussian Instance Tracing
Hongyu Shen, Junfeng Ni, Yixin Chen +3
We address the challenge of lifting 2D visual segmentation to 3D in Gaussian Splatting. Existing methods often suffer from inconsistent 2D masks across viewpoints and produce noisy…
DreamArt: Generating Interactable Articulated Objects from a Single Image
Ruijie Lu, Yu Liu, Jiaxiang Tang +6
Generating articulated objects, such as laptops and microwaves, is a crucial yet challenging task with extensive applications in Embodied AI and AR/VR. Current image-to-3D methods…
Decompositional Neural Scene Reconstruction with Generative Diffusion Prior
Junfeng Ni, Yu Liu, Ruijie Lu +4
Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture of all objects within, is intriguing for downstream applications but remains challenging, par…
ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting
Yu Liu, Baoxiong Jia, Ruijie Lu +3
Building articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the ac…
TACO: Taming Diffusion for in-the-wild Video Amodal Completion
Ruijie Lu, Yixin Chen, Yu Liu +5
Humans can infer complete shapes and appearances of objects from limited visual cues, relying on extensive prior knowledge of the physical world. However, completing partially obse…
MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes
Ruijie Lu, Yixin Chen, Junfeng Ni +5
Repurposing pre-trained diffusion models has been proven to be effective for NVS. However, these methods are mostly limited to a single object; directly applying such methods to co…
PhyRecon: Physically Plausible Neural Scene Reconstruction
Junfeng Ni, Yixin Chen, Bohan Jing +7
We address the issue of physical implausibility in multi-view neural reconstruction. While implicit representations have gained popularity in multi-view 3D reconstruction, previous…
Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture
Yixin Chen, Junfeng Ni, Nan Jiang +3
Reconstructing detailed 3D scenes from single-view images remains a challenging task due to limitations in existing approaches, which primarily focus on geometric shape recovery, o…