203 citations · 378 across the 9 of their papers we have counts for
11 papers
Diversify Diffusion with Temperature Sampling and Variance-Corrective Time Shifting
Peizhuo Li, Emre Aksan, Alexandru-Eugen Ichim +2
Diffusion models faithfully reproduce their training distribution, but also inherit its imbalances and leave rare or under-represented modes hard to reach. A natural inference-time…
Dancing Points: Synthesizing Ballroom Dancing with Three-Point Inputs
Peizhuo Li, Sebastian Starke, Yuting Ye +1
Ballroom dancing is a structured yet expressive motion category. Its highly diverse movement and complex interactions between leader and follower dancers make the understanding and…
WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds
Peizhuo Li, Sebastian Starke, Yuting Ye +1
We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike…
Neural Garment Dynamics via Manifold-Aware Transformers
Peizhuo Li, Tuanfeng Y. Wang, Timur Levent Kesdogan +2
Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often…
Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Qingqing Zhao, Peizhuo Li, Wang Yifan +2
Creating believable motions for various characters has long been a goal in computer graphics. Current learning-based motion synthesis methods depend on extensive motion datasets, w…
Example-based Motion Synthesis via Generative Motion Matching
Weiyu Li, Xuelin Chen, Peizhuo Li +2
We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, whi…
MoDi: Unconditional Motion Synthesis from Diverse Data
Sigal Raab, Inbal Leibovitch, Peizhuo Li +3
The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains challenging…
GANimator: Neural Motion Synthesis from a Single Sequence
Peizhuo Li, Kfir Aberman, Zihan Zhang +2
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements o…
Learning Skeletal Articulations with Neural Blend Shapes
Peizhuo Li, Kfir Aberman, Rana Hanocka +3
Animating a newly designed character using motion capture (mocap) data is a long standing problem in computer animation. A key consideration is the skeletal structure that should c…
Skeleton-Aware Networks for Deep Motion Retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski +3
We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Imp…
Saliency based Semi-supervised Learning for Orbiting Satellite Tracking
Peizhuo Li, Yunda Sun, Xue Wan
The trajectory and boundary of an orbiting satellite are fundamental information for on-orbit repairing and manipulation by space robots. This task, however, is challenging owing t…