arXiv:2503. 24009v3 Announce Type: replace-cross Abstract: Realistic simulation is critical for applications ranging from robotics to animation.
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arXiv:2609.19142v1 Announce Type: new Abstract: World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse...
ParticleSplat is a self‑supervised, object‑centric learning framework that extends the Deep Latent Particles (DLP) model into 3D by representing scenes as latent particles mapped to 3D Gaussian splats. It jointly encodes multiple camera views into a shared 3D latent space, enabling unsupervised learning of object masks and controllable 3D scene editing such as moving objects by manipulating latent particles. Experiments on simulated and real‑world datasets demonstrate that this 3D representation improves performance on downstream robotic manipulation tasks.
World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into do...
We present ParticleSplat, a self-supervised object-centric learning method that decomposes scenes into a set of latent ''particles'' representing semantic entities through feedforward 3D Gaussian Spla...
arXiv:2608.31025v1 Announce Type: new Abstract: Inferring object dynamics from visual observations is essential for intelligent agents to reason about and interact with the physical world, yet remain...
arXiv:2606. 19451v1 Announce Type: new Abstract: We introduce 3D-DLP, a self-supervised object-centric representation learning model that decomposes scene-level RGB-D or voxel observations into a set of 3D latent particles.