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20242026
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cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

When Do Diffusion Models learn to Generate Multiple Objects?

Yujin Jeong, Arnas Uselis, Iro Laina +2

Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical evidence of these failures, th…

cs.CV2026

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…

cs.CV2026

Reflect3r: Single-View 3D Stereo Reconstruction Aided by Mirror Reflections

Jing Wu, Zirui Wang, Iro Laina +1

Mirror reflections are common in everyday environments and can provide stereo information within a single capture, as the real and reflected virtual views are visible simultaneousl…

cs.CV2026

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…