3 citations · 5 across the 11 of their papers we have counts for
12 papers
FlowHMR: Physically Plausible Motion Capture from Video
Zhanke Wang, Chengfeng Zhao, Qing Shuai +8
We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typically regress human motion dire…
AnimateCanvas: Learning Implicit Motion Planning from Composable Kinematic Cues
Zeyu Ling, Di Kang, Qing Shuai +7
Professional character animation requires both natural motion and precise, versatile control. For example, creators often define the timing of a specified action, control the motio…
GenTrack: Physical Alignment for Robot-Native Motion Generation and Zero-Shot Humanoid Tracking
Zeyu Ling, Xinyao Yu, Renye Yan +4
General-purpose humanoid trackers can execute diverse references, but their zero-shot coverage depends on large embodied corpora that are costly to extend. Text-to-motion generator…
NeutronSparse: Coordinating Heterogeneous Engines for Sparse Matrix Multiplication on NPUs
Xin Ai, Zeyu Ling, Hao Yuan +4
Sparse matrix-matrix multiplication (SpMM) is a fundamental data operation for large-scale sparse data processing. With NPUs increasingly deployed in data centers for their perform…
PRISM: Streaming Human Motion Generation with Per-Joint Latent Decomposition
Zeyu Ling, Qing Shuai, Teng Zhang +3
Text-to-motion generation has advanced with larger corpora and stronger generators, yet many models still rely on holistic frame- or clip-level latents that entangle trajectory, or…
SyncLipMAE: Contrastive Masked Pretraining for Audio-Visual Talking-Face Representation
Zeyu Ling, Xiaodong Gu, Jiangnan Tang +1
We introduce SyncLipMAE, a self-supervised pretraining framework for talking-face video that learns synchronization-aware and transferable facial dynamics from unlabeled audio-visu…
EPIC: Efficient Prompt Interaction for Text-Image Classification
Xinyao Yu, Hao Sun, Zeyu Ling +5
In recent years, large-scale pre-trained multimodal models (LMMs) generally emerge to integrate the vision and language modalities, achieving considerable success in multimodal tas…
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism
Xin Ai, Hao Yuan, Zeyu Ling +6
Graph neural networks (GNNs) have emerged as a promising direction. Training large-scale graphs that relies on distributed computing power poses new challenges. Existing distribute…
VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension
Zeyu Ling, Bo Han, Shiyang Li +3
Large language models (LLMs) are, by design, inherently capable of multi-task learning: through a unified next-token prediction paradigm, they can naturally address a wide variety…
MCM: Multi-condition Motion Synthesis Framework
Zeyu Ling, Bo Han, Yongkang Wongkan +3
Conditional human motion synthesis (HMS) aims to generate human motion sequences that conform to specific conditions. Text and audio represent the two predominant modalities employ…
EnchantDance: Unveiling the Potential of Music-Driven Dance Movement
Bo Han, Teng Zhang, Zeyu Ling +1
The task of music-driven dance generation involves creating coherent dance movements that correspond to the given music. While existing methods can produce physically plausible dan…
MCM: Multi-condition Motion Synthesis Framework for Multi-scenario
Zeyu Ling, Bo Han, Yongkang Wong +2
The objective of the multi-condition human motion synthesis task is to incorporate diverse conditional inputs, encompassing various forms like text, music, speech, and more. This e…