collaborators

6 papers

cs.GR2026

MoGeFlow: Flowing Through Motion Codebook Geometry for Text-to-Motion Generation

Pengcheng Fang, Tengjiao Sun, Xiaoyu Zhan +2

Vector-quantized motion tokenizers provide a compact discrete interface for text-to-motion generation, but most motion-code priors treat code indices as unordered categorical label…

cs.CV2026

KV-Control: Parameter-Efficient K/V Injection for Trajectory-Controlled Text-to-Motion

Tengjiao Sun, Pengcheng Fang, Xiaoyu Zhan +4

Text-conditioned 3D human motion models now synthesize plausible motions from prompts, but practical animation and embodied-agent workflows rarely stop at text: a character may nee…

cs.GR2026

AnchorRoute: Human Motion Synthesis with Interval-Routed Sparse Contro

Pengcheng Fang, Tengjiao Sun, Dongjie Fu +4

Sparse anchors provide a compact interface for human motion authoring: users specify a few root positions, planar trajectory samples, or body-point targets, while the system synthe…

cs.GR2026

UMo: Unified Sparse Motion Modeling for Real-Time Co-Speech Avatars

Xiaoyu Zhan, Xinyu Fu, Chenghao Yang +9

Speech-driven gestures and facial animations are fundamental to expressive digital avatars in games, virtual production, and interactive media. However, existing methods are either…

cs.CV2026

MOGO: Residual Quantized Hierarchical Causal Transformer for High-Quality and Real-Time 3D Human Motion Generation

Dongjie Fu, Tengjiao Sun, Pengcheng Fang +2

Recent advances in transformer-based text-to-motion generation have led to impressive progress in synthesizing high-quality human motion. Nevertheless, jointly achieving high fidel…

cs.CV2024

Mogo: RQ Hierarchical Causal Transformer for High-Quality 3D Human Motion Generation

Dongjie Fu

In the field of text-to-motion generation, Bert-type Masked Models (MoMask, MMM) currently produce higher-quality outputs compared to GPT-type autoregressive models (T2M-GPT). Howe…