Search Results for author:
Found 8 papers, 7 papers with code
MotionLLM: Understanding Human Behaviors from Human Motions and Videos
This study delves into the realm of multi-modality (i. e., video and motion modalities) human behavior understanding by leveraging the powerful capabilities of Large Language Models (LLMs).
MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model
By employing one-step (or few-step) inference, we further improve the runtime efficiency of the motion latent diffusion model for motion generation.
Ranked #19 on
Motion Synthesis
on HumanML3D
One-Shot Learning as Instruction Data Prospector for Large Language Models
Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance.
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
To tackle this challenge and enhance the robustness of deep learning models against label noise in graph-based tasks, we propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE).
HumanTOMATO: Text-aligned Whole-body Motion Generation
This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent facial expressions, hand gestures, and body motions simultaneously.
Ranked #1 on
Motion Synthesis
on Motion-X
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
However, since the prompts need to be sampled from a large volume of annotated examples, finding the right prompt may result in high annotation costs.
HumanMAC: Masked Motion Completion for Human Motion Prediction
In the training stage, we learn a motion diffusion model that generates motions from random noise.
AnomMAN: Detect Anomaly on Multi-view Attributed Networks
It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks.
