3 papers
cs.GR2026
Two2Four: Generative Quadruped Puppeteering from Human Motion
Fatemeh Zargarbashi, Zehong Qiu, Dhruv Agrawal +4
Realistic animal motion for virtual production is typically obtained either through motion capture of highly trained performers who accurately mimic animal behavior, or by retarget…
cs.CV2026
VQ-Style: Disentangling Style and Content in Motion with Residual Quantized Representations
Fatemeh Zargarbashi, Dhruv Agrawal, Jakob Buhmann +3
Human motion data is inherently rich and complex, containing both semantic content and subtle stylistic features that are challenging to model. We propose a novel method for effect…
cs.RO2024
RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
Fatemeh Zargarbashi, Jin Cheng, Dongho Kang +2
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level obj…