activity
20182026
most citedLearning to Generate Diverse Dance Motions with Transformer

74 citations · 106 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV2026

TrajLoom: Dense Future Trajectory Generation from Video

Zewei Zhang, Jia Jun Cheng Xian, Kaiwen Liu +4

Predicting future motion is crucial in video understanding and controllable video generation. Dense point trajectories are a compact, expressive motion representation, but modeling…

cs.CV2021

LSD-StructureNet: Modeling Levels of Structural Detail in 3D Part Hierarchies

Dominic Roberts, Ara Danielyan, Hang Chu +2

Generative models for 3D shapes represented by hierarchies of parts can generate realistic and diverse sets of outputs. However, existing models suffer from the key practical limit…

cs.CV202120 cited

House-GAN++: Generative Adversarial Layout Refinement Networks

Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang +3

This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relationa…

cs.CV20205 cited

Expressive Telepresence via Modular Codec Avatars

Hang Chu, Shugao Ma, Fernando De la Torre +2

VR telepresence consists of interacting with another human in a virtual space represented by an avatar. Today most avatars are cartoon-like, but soon the technology will allow vide…

cs.CV202074 cited

Learning to Generate Diverse Dance Motions with Transformer

Jiaman Li, Yihang Yin, Hang Chu +4

With the ongoing pandemic, virtual concerts and live events using digitized performances of musicians are getting traction on massive multiplayer online worlds. However, well chore…

cs.CV20196 cited

Neural Turtle Graphics for Modeling City Road Layouts

Hang Chu, Daiqing Li, David Acuna +6

We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the…