372 citations · 573 across the 34 of their papers we have counts for
36 papers
Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
Seong Jong Yoo, Siyuan Peng, Felix Gu +2
Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressive full-body dynamics. While exi…
Real2SAM2Real: Generative 3D Caches as Complementary Context for Video Diffusion
Jiayi Wu, Haoming Cai, Cornelia Fermuller +2
Video Diffusion Models (VDMs) synthesize high-quality videos, but precise camera and motion control remains challenging when large viewpoint shifts, changing occlusions, or complex…
ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation
Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh +3
Vision-based tactile sensing is widely used in perception, reconstruction, and robotic manipulation, yet collecting large-scale tactile data remains costly due to diverse sensor-ob…
Learning Normal Flow Directly From Event Neighborhoods
Dehao Yuan, Levi Burner, Jiayi Wu +4
Event-based motion field estimation is an important task. However, current optical flow methods face challenges: learning-based approaches, often frame-based and relying on CNNs, l…
Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation
Jingxi Chen, Brandon Y. Feng, Haoming Cai +6
Video Frame Interpolation aims to recover realistic missing frames between observed frames, generating a high-frame-rate video from a low-frame-rate video. However, without additio…
FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors
Amir-Hossein Shahidzadeh, Gabriele Caddeo, Koushik Alapati +3
In this paper, we tackle the problem of estimating 3D contact forces using vision-based tactile sensors. In particular, our goal is to estimate contact forces over a large range (u…