5 citations · 15 across the 41 of their papers we have counts for
47 papers
JtT: Rethinking Point Trajectory Diffusion for Diverse and Rigid Motion from an Image
Gabrijel Boduljak, Yushi Lan, Christian Rupprecht +1
We study forecasting rigid-body motion from a single image: generating diverse and physically plausible future trajectories for arbitrary points in a scene, without building rigidi…
SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion
Paul Engstler, Iro Laina, Christian Rupprecht +1
We present SynCity 3000, a framework for generating 3D scenes that are globally coherent while enabling fine-grained layout control. Building on the ability of current image-to-3D…
PhysiFormer: Learning to Simulate Mechanics in World Space
Yiming Chen, Yushi Lan, Andrea Vedaldi
We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion. Unlike video world models that operate in view-dependent pixel space, PhysiFormer represe…
Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control
Ruining Li, Yuxin Yao, Matt Zhou +5
Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations. Recent neural networks can estimate the articulated structure of 3D objects, but…
VGGT-
Jianyuan Wang, Minghao Chen, Shangzhan Zhang +7
Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also providing geometry-aware features use…
Articraft: An Agentic System for Scalable Articulated 3D Asset Generation
Matt Zhou, Ruining Li, Xiaoyang Lyu +6
A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large language models (LLMs) to close…
Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning
Ido Sobol, Kihyuk Sohn, Yoav Blum +4
We often aim to generate images that are both photorealistic and 3D-consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine-…
Syn4D: A Multiview Synthetic 4D Dataset
Zeren Jiang, Yushi Lan, Yihang Luo +8
Dense 3D reconstruction and tracking of dynamic scenes from monocular video remains an important open challenge in computer vision. Progress in this area has been constrained by th…
NOVA3R: Non-pixel-aligned Visual Transformer for Amodal 3D Reconstruction
Weirong Chen, Chuanxia Zheng, Ganlin Zhang +2
We present NOVA3R, an effective approach for non-pixel-aligned 3D reconstruction from a set of unposed images in a feed-forward manner. Unlike pixel-aligned methods that tie geomet…
LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis
Stanislaw Szymanowicz, Minghao Chen, Jianyuan Wang +2
Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive bi…
CoWTracker: Tracking by Warping instead of Correlation
Zihang Lai, Eldar Insafutdinov, Edgar Sucar +1
Dense point tracking is a fundamental problem in computer vision, with applications ranging from video analysis to robotic manipulation. State-of-the-art trackers typically rely on…
V-DPM: 4D Video Reconstruction with Dynamic Point Maps
Edgar Sucar, Eldar Insafutdinov, Zihang Lai +1
Powerful 3D representations such as DUSt3R invariant point maps, which encode 3D shape and camera parameters, have significantly advanced feed forward 3D reconstruction. While poin…
Mesh4D: 4D Mesh Reconstruction and Tracking from Monocular Video
Zeren Jiang, Chuanxia Zheng, Iro Laina +2
We propose Mesh4D, a feed-forward model for monocular 4D mesh reconstruction. Given a monocular video of a dynamic object, our model reconstructs the object's complete 3D shape and…
LaVR: Scene Latent Conditioned Generative Video Trajectory Re-Rendering using Large 4D Reconstruction Models
Mingyang Xie, Numair Khan, Tianfu Wang +8
Given a monocular video, the goal of video re-rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrical…
VFMF: World Modeling by Forecasting Vision Foundation Model Features
Gabrijel Boduljak, Yushi Lan, Christian Rupprecht +1
Forecasting from partial observations is central to world modeling. Many recent methods represent the world through images, and reduce forecasting to stochastic video generation. A…
Particulate: Feed-Forward 3D Object Articulation
Ruining Li, Yuxin Yao, Chuanxia Zheng +4
We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion const…
What Happens Next? Anticipating Future Motion by Generating Point Trajectories
Gabrijel Boduljak, Laurynas Karazija, Iro Laina +2
We consider the problem of forecasting motion from a single image, i.e., predicting how objects in the world are likely to move, without the ability to observe other parameters suc…
AutoPartGen: Autogressive 3D Part Generation and Discovery
Minghao Chen, Jianyuan Wang, Roman Shapovalov +6
We introduce AutoPartGen, a model that generates objects composed of 3D parts in an autoregressive manner. This model can take as input an image of an object, 2D masks of the objec…
Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey
Jiahui Zhang, Yuelei Li, Anpei Chen +15
3D reconstruction and view synthesis are foundational problems in computer vision, graphics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and di…
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos
Vadim Tschernezki, Diane Larlus, Iro Laina +1
Computer vision is largely based on 2D techniques, with 3D vision still relegated to a relatively narrow subset of applications. However, by building on recent advances in 3D model…
VMem: Consistent Interactive Video Scene Generation with Surfel-Indexed View Memory
Runjia Li, Philip Torr, Andrea Vedaldi +1
We propose a novel memory module for building video generators capable of interactively exploring environments. Previous approaches have achieved similar results either by out-pain…
Triangle Splatting for Real-Time Radiance Field Rendering
Jan Held, Renaud Vandeghen, Adrien Deliege +7
The field of computer graphics was revolutionized by models such as Neural Radiance Fields and 3D Gaussian Splatting, displacing triangles as the dominant representation for photog…
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction
Weirong Chen, Ganlin Zhang, Felix Wimbauer +4
Traditional SLAM systems, which rely on bundle adjustment, struggle with highly dynamic scenes commonly found in casual videos. Such videos entangle the motion of dynamic elements,…
Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
Zeren Jiang, Chuanxia Zheng, Iro Laina +2
We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic priors captured by large-scale…