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20212026
most citedMedia2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance

3 citations · 10 across the 6 of their papers we have counts for

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cs.CV2026

MIND: Multi-Scale Intent Diffusion for Text-Driven Physics-Based Humanoid Control

Bin Li, Ruichi Zhang, Han Liang +4

Enabling physics-based humanoids to execute diverse behaviors from high-level textual commands remains a significant challenge. Existing methods typically follow either a two-stage…

cs.CV2025

InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs

Bin Li, Ruichi Zhang, Han Liang +6

Humanoid agents are expected to emulate the complex coordination inherent in human social behaviors. However, existing methods are largely confined to single-agent scenarios, overl…

cs.CV20243 cited

LLaVA-SLT: Visual Language Tuning for Sign Language Translation

Han Liang, Chengyu Huang, Yuecheng Xu +6

In the realm of Sign Language Translation (SLT), reliance on costly gloss-annotated datasets has posed a significant barrier. Recent advancements in gloss-free SLT methods have sho…

cs.CV20243 cited

Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance

Qingcheng Zhao, Pengyu Long, Qixuan Zhang +6

The synthesis of 3D facial animations from speech has garnered considerable attention. Due to the scarcity of high-quality 4D facial data and well-annotated abundant multi-modality…

cs.CV2023

OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers

Han Liang, Jiacheng Bao, Ruichi Zhang +6

We have recently seen tremendous progress in realistic text-to-motion generation. Yet, the existing methods often fail or produce implausible motions with unseen text inputs, which…

cs.CV2023

InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions

Han Liang, Wenqian Zhang, Wenxuan Li +2

We have recently seen tremendous progress in diffusion advances for generating realistic human motions. Yet, they largely disregard the multi-human interactions. In this paper, we…