Tingting Liao
廖婷婷
Research
I work on world models for embodied AI — real-time, action-conditioned video generation, and using generated worlds as data engines and evaluators for robot policies. I came to this from generative video and 3D, building controllable models of humans, objects and scenes, which is where my interest in physical consistency and controllability comes from.
World Models · Video Generation · Embodied AI
Education
PhD·MBZUAI
MSc·CASIA
BEng·Wuhan Polytechnic University
News
- MMGR accepted to EMNLP 2026
- Steering Video Diffusion Transformers with Massive Activations released on arXiv
- Started a research internship at the Institute of Foundation Models, MBZUAI, on real-time interactive world models
- Character Mixing for Video Generation released on arXiv
- Started a research internship at Adobe Research
- SOAP accepted to SIGGRAPH 2025
- TADA! and TeCH accepted to 3DV 2024
Experience
University of Technology Sydney
Visiting Student
2018.12 — 2019.03
Xiaohongshu
Research Intern
2022.02 — 2023.08
Westlake University
Visiting Student
2025.02 — 2025.04
Adobe Research
Research Intern
2025.05 — 2025.08
IFM, MBZUAI
Research Intern
2026.01 — Present
Publications
TADA! Text to Animatable Digital Avatars
3DV 2024
#3 Most Influential 3DV 2024 PaperGenerates animatable 3D avatars from text alone, with high-quality geometry and texture.
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
3DV 2024
#6 Most Influential 3DV 2024 PaperReconstructs a fully clothed human from a single image, using text guidance to hallucinate the regions the camera never saw.
Open Source
Dream2DGS
Image-to-3D with 2D Gaussian splatting — surfel-based reconstruction that keeps geometry crisp where volumetric splatting goes soft.
MirrorGS
Re-implementation of mirror-aware Gaussian splatting, so reflective surfaces reconstruct as mirrors instead of as holes in the scene.
DreamScene360
Re-implementation of text to 360° 3D scenes — a full panoramic environment generated from a single prompt.