1 citations · 3 across the 12 of their papers we have counts for
15 papers
MOMO: Human Motion from Moving Sounds
Deli Cai, Shuyang Xu, Zhiyang Dou +4
Human responses to sound depend not only on its semantic content, but also on the evolving spatial relationship between source and listener, conveyed through spatial audio and its…
DynaConTalk: Wavelet-Constrained Diffusion for Long-Form and Controllable Holistic Co-Speech 3D Motion
Yifei Zhu, Yangyang Cai, Mingyi Shi +4
Holistic co-speech animation is prone to averaging in both motion representation and speech conditioning. In coordinate-space diffusion, slow body posture, mid-frequency gesture st…
CurveCodec 2: Skeleton-agnostic animation compression with a learned entropy model
Mingyi Shi, Huancheng Lin, Xuelin Chen +1
Skeletal motion is stored as every joint's transform at every frame, yet most of it is implied by the body rather than by what the motion is about. Compression is one way to ask wh…
MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation
Yifei Zhu, Mingyi Shi, Yangyang Cai +3
Text-to-motion generation must produce motions that are semantically correct, temporally coherent, and physically plausible. A natural approach is to first project motion data into…
Prior-First, Condition-Second: Scalable and Controllable Hand Motion Completion
Mingyi Shi, Xuelin Chen, Taku Komura
Synthesizing hand motion that matches the full body motion and the semantic labels is a difficult task due to their high degrees of freedom and the lack of semantic labels. To cope…
EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents
Wenjia Wang, Liang Pan, Huaijin Pi +8
Human behaviors in the real world naturally encode rich, long-term contextual information that can be leveraged to train embodied agents for perception, understanding, and acting.…
UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors
Xiaoyan Cong, Zekun Li, Zhiyang Dou +9
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets…
InterAct: A Large-Scale Dataset of Dynamic, Expressive and Interactive Activities between Two People in Daily Scenarios
Leo Ho, Yinghao Huang, Dafei Qin +5
We address the problem of accurate capture of interactive behaviors between two people in daily scenarios. Most previous works either only consider one person or solely focus on co…
MotionPersona: Real-Time Locomotion Control across Personas, Bodies, and Styles
Mingyi Shi, Wei Liu, Jidong Mei +4
We present MotionPersona, a generative framework for character-aware locomotion control, in which the motion for a command depends on the captured persona, the body shape, and the…
Zero-Shot Human-Object Interaction Synthesis with Multimodal Priors
Yuke Lou, Yiming Wang, Zhen Wu +4
Human-object interaction (HOI) synthesis is important for various applications, ranging from virtual reality to robotics. However, acquiring 3D HOI data is challenging due to its c…
It Takes Two: Real-time Co-Speech Two-person's Interaction Generation via Reactive Auto-regressive Diffusion Model
Mingyi Shi, Dafei Qin, Leo Ho +4
Conversational scenarios are very common in real-world settings, yet existing co-speech motion synthesis approaches often fall short in these contexts, where one person's audio and…
CHOICE: Coordinated Human-Object Interaction in Cluttered Environments for Pick-and-Place Actions
Jintao Lu, He Zhang, Yuting Ye +3
Animating human-scene interactions such as pick-and-place tasks in cluttered, complex layouts is a challenging task, with objects of a wide variation of geometries and articulation…
Instant Facial Gaussians Translator for Relightable and Interactable Facial Rendering
Dafei Qin, Hongyang Lin, Qixuan Zhang +7
We propose GauFace, a novel Gaussian Splatting representation, tailored for efficient animation and rendering of physically-based facial assets. Leveraging strong geometric priors…
InterAct: Capture and Modelling of Realistic, Expressive and Interactive Activities between Two Persons in Daily Scenarios
Yinghao Huang, Leo Ho, Dafei Qin +2
We address the problem of accurate capture and expressive modelling of interactive behaviors happening between two persons in daily scenarios. Different from previous works which e…
Taming Diffusion Probabilistic Models for Character Control
Rui Chen, Mingyi Shi, Shaoli Huang +3
We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding…