145 citations · 151 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 5 cited
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
Seung Wook Kim, Bradley Brown, Kangxue Yin +6
Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce…
cs.CV2023★ 1 cited
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
Davis Rempe, Zhengyi Luo, Xue Bin Peng +5
We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. We draw on recent advances in gui…
cs.LG2021★ 145 cited
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Zhisheng Xiao, Karsten Kreis, Arash Vahdat
A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing three key requirements including: hi…