55 citations · 131 across the 9 of their papers we have counts for
17 papers
Value function estimation using conditional diffusion models for control
Bogdan Mazoure, Walter Talbott, Miguel Angel Bautista +3
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As…
Manifold Diffusion Fields
Ahmed A. Elhag, Yuyang Wang, Joshua M. Susskind +1
We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion models of data in general non-Euclidean geometries. Leveraging insights from spectral geo…
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2
Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational au…
GAUDI: A Neural Architect for Immersive 3D Scene Generation
Miguel Angel Bautista, Pengsheng Guo, Samira Abnar +9
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle thi…
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
Zhenpei Yang, Zhile Ren, Miguel Angel Bautista +3
Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate cam…
Fast and Explicit Neural View Synthesis
Pengsheng Guo, Miguel Angel Bautista, Alex Colburn +4
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects. We propose a simple yet effective approach that is neither continuo…