Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Spatial layout planning in architecture requires a deep understanding of topological spatial
relationships, yet the process remains repetitive and laborious for designers. However …

Attaining a high degree of user controllability in visual generation often requires intricate,
fine-grained inputs like layouts. However, such inputs impose a substantial burden on users …

Generating realistic 3D worlds occupied by moving humans has many applications in
games, architecture, and synthetic data creation. But generating such scenes is expensive …

Abstract We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a
new, large-scale, and compre-hensive repository of synthetic indoor scenes highlighted by …

Humans universally dislike the task of cleaning up a messy room. If machines were to help
us with this task, they must understand human criteria for regular arrangements, such as …

The ability to synthesize realistic and diverse indoor furniture layouts automatically or based
on partial input, unlocks many applications, from better interactive 3D tools to data synthesis …

This paper proposes a novel graph-constrained generative adversarial network, whose
generator and discriminator are built upon relational architecture. The main idea is to …

This paper proposes a generative adversarial layout refinement network for automated
floorplan generation. Our architecture is an integration of a graph-constrained relational …

Recently, there has been growing interest in develo** learning-based methods to detect
and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids …