Ziyi Chang

From Kinematics to Physics and Embodiment

I study human motion at multiple levels of realism, from kinematic motion synthesis to physics-aware interaction and embodied agents. This connects visually plausible animation with contact-rich interaction, physical constraints, and agents that can act within shared environments.

From Individuals to Interactions and Crowds

My work expands motion modelling from individual characters to dense interaction and crowd-level behaviour. I am interested in how local motion, pairwise or group interaction, and large-scale collective patterns can be represented within a coherent modelling framework.

From Generation to Understanding and Intervention

I view generative modelling as one part of a broader reasoning pipeline. The goal is not only to synthesize plausible motion, but also to understand interaction patterns and support intervention, planning, or analysis in dynamic human-centred environments.

Trustworthy, Controllable, and Interpretable Deployment

Across these axes, I aim to make motion intelligence robust, controllable, and interpretable. Building on my work on adversarial attacks and trustworthy AI, I study how motion models behave under perturbations, ambiguous observations, and safety-critical deployment conditions.

GROWD: Generative Modelling of Dense Crowds under Multiscale Observations

Bienvenüe+ MSCA-COFUND Fellowship supporting a 24-month independent research programme that I proposed and developed, hosted by the VirtUs team at Inria Rennes.

• Funder: Région Bretagne (Bienvenüe+ MSCA-COFUND)
• Period: Nov 2026 – Oct 2028
• Total funding: €131,760
• Role: Project Lead / Research Fellow

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Cross-Institutional Research Capacity Development in Human-Robot Interaction

SMU-Durham project on research capacity development in human-robot interaction.

• Funding Source: Singapore Management University / Durham University, Singapore / UK
• Period: 2025 – 2026
• Reference Number: 3787041; Value: £15,000
• Role: Research Assistant / Project Contributor

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PhD Co-Supervision

Co-supervising a PhD student with Prof. Hubert P. H. Shum at Durham University on interactive motion modelling and character animation.

• Providing research guidance on problem formulation, experimental design, paper writing, and revision.
• Co-supervised work has led to accepted papers at Eurographics 2026 and SIGGRAPH Asia 2025, including Physics-Based Motion Tracking of Contact-Rich Interacting Characters and Motion In-Betweening for Densely Interacting Characters.
• This supervision experience directly informs my broader research agenda on multi-character interaction and socially intelligent embodied agents.

Final Year Project Supervision

Co-supervised undergraduate student Edmund with Prof. Hubert P. H. Shum on stylized human motion generation.

• The project explored denoising diffusion probabilistic models for stylized human walking synthesis; I supported the project direction, experiment design, and paper development.
• The project led to the paper Denoising Diffusion Probabilistic Models for Styled Walking Synthesis.
• The work connects undergraduate research supervision with my broader interests in generative human motion and character animation.

University Teaching

Delivered practical and workshop sessions for undergraduate and postgraduate modules at Durham University.

• Worked in teaching teams with lecturers, reporting to Dr Barnaby Martin.
• Teaching areas include programming, data science, computational thinking, data analytics, and machine learning.
• Supported mixed-background cohorts through hands-on sessions, debugging support, assessment preparation, and project guidance.

Conference Organisation

Supporting Chair of the 21st ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA 2022).

• Led a 7-person support team.
• Coordinated with conference chairs, industrial partners, academic participants, business contacts, and service suppliers.

Outreach and Public Engagement

Co-designed and delivered robotics and AI workshops for children at Bullion Hall, UK.

• Promoted inclusive participation in STEM, with particular attention to encouraging girls to explore robotics and AI.
• Delivered accessible AI education sessions for older adults, focusing on practical understanding and trust in everyday AI technologies.

Invited Talks and Seminars

Coming soon.