Functionality, Articulation, and Interaction Workshop

On modeling and generating 3D objects that constitute interactive environments.

Functionality, Articulation, and Interaction · workshop teaser

01Workshop overview

Recent advances in 3D generative AI have achieved impressive geometric and textural fidelity. Yet three critical dimensions remain overlooked: functionality, articulation, and interaction. Generated 3D content must not only look realistic — it must move correctly, respond to physical forces, and afford meaningful engagement with agents and environments.

The FAI workshop brings together researchers across computer vision, graphics, robotics, and machine learning to unify work on functional understanding, articulated reconstruction, part decomposition, and physical grounding. We position functionality as a first-class concept across the full pipeline — perceived from visual observations, represented through explicit structure, and realized through physically grounded generation.

02Topics of interest

Visual understanding — Learning functional relations from visual data; function-aware generation.
Spatial modelling — Parts, relations, motion parameters, affordances; structure that enables motion.
Physical grounding — Physics in generative models; physical constraints and realistic dynamics.
Foundation models — Aligning visual, spatial, physical understanding with LLM/VLM knowledge.

03Call for papers

Submission deadline
July 24, 2026
Author notification
August 7, 2026
Camera-ready
August 15, 2026

We welcome submissions on modeling and generation of functional and interactable 3D assets. Both short papers (7 pages) and full papers (14 pages) are welcome, in ECCV format (excluding references). All accepted submissions will be presented as posters. Papers will not be included in the official proceedings.

04Schedule & Speakers

Welcome & introduction
Opening remarks from the organizers.
14:00 — 14:15
Andrea Vedaldi
University of Oxford · Meta
Coopting coding agents for physical intelligence
Physical intelligence is the problem of solving problems in the physical world, and is one of the most exciting frontiers of computer vision and AI in general. In this talk, I will review Articraft, our recent attempt at coopting powerful language models with coding capabilities for spatial intelligence. I will consider the problem of generating 3D objects with a complex, articulated structure. I will reduce this task to that of writing a program that, using a specialised SDK, generates the 3D model. I will then introduce an harness that allows a language model to edit this program, compile it into a 3D object, and obtain information about the latter, including probing and measuring it in different ways, as well as designing geometric tests to automatically assess the validity of the construct. Closed in a loop, the harness receives feedback from probing and testing the 3D model and corrects the program to improve it. By avoiding explicit visual feedback, this results in a fast and efficient agent that I will use to create Articraft-10k, a large, curated library of 3D articulated objects for the benefit of the community.
14:15 — 14:40
Wei-Chiu Ma
Cornell University
Title TBD
Abstract TBD
14:40 — 15:05
Evangelos Kalogerakis
Technical University of Crete
Title TBD
Abstract TBD
15:05 — 15:30
Poster session
Accepted papers presented in poster format.
15:30 — 16:00
Angela Dai
Technical University of Munich
Title TBD
Abstract TBD
16:05 — 16:30
Ziwei Liu
Nanyang Technological University
Title TBD
Abstract TBD
16:30 — 16:55
Mikaela Angelina Uy
NVIDIA Research
Title TBD
Abstract TBD
16:55 — 17:20
Closing panel
All invited speakers · discussion and Q&A.
17:20 — 18:00

05Organizers

Main organizers

Jiayi Liu
Simon Fraser University
Mingrui Zhao
Simon Fraser University
Denys Iliash
Simon Fraser University
Kai Wang
ShanghaiTech University
Hanxiao Jiang
Columbia University

Advisors

Angel X. Chang
Simon Fraser University
Ruizhen Hu
Shenzhen University
Ali Mahdavi-Amiri
Simon Fraser University
Manolis Savva
Simon Fraser University
Hao (Richard) Zhang
Simon Fraser University