10 papers
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…
PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media
Fuhao Li, Shaofeng You, Jiagao Hu +6
Evaluating object removal in images and videos remains challenging because the task is inherently one-to-many, yet existing metrics frequently disagree with human perception. Full-…
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…
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…
SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields
Yu Liu, Baoxiong Jia, Yixin Chen +1
The ability to distill object-centric abstractions from intricate visual scenes underpins human-level generalization. Despite the significant progress in object-centric learning me…