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Joan Lasenby

7 accepted papers

2026

Particulate: Feed-Forward 3D Object Articulation

CVPR 2026

We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these paramet

Cited by 0SourcecodeScholar
2026

SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time

CVPR 2026

We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter both the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for conti

Cited by 0SourcecodeScholar
2025

Fengbo: a Clifford Neural Operator pipeline for 3D PDEs in Computational Fluid Dynamics

ICLR 2025poster

We introduce Fengbo, a pipeline entirely in Clifford Algebra to solve 3D partial differential equations (PDEs) specifically for computational fluid dynamics (CFD). Fengbo is an architecture composed of only 3D convolutional and Fourier Neural Operator (FNO) layers, all working in 3D Clifford Algebra…

Cited by 1SourcePDFScholar
2025

LiteReality: Graphic-Ready 3D Scene Reconstruction from RGB-D Scans

NeurIPS 2025poster

We propose LiteReality, a novel pipeline that converts RGB-D scans of indoor environments into compact, realistic, and interactive 3D virtual replicas. LiteReality not only reconstructs scenes that visually resemble reality but also supports key features essential for graphics pipelines, such as obj…

Cited by 0SourceScholar
2023

Evaluating Self-Supervised Learning for Molecular Graph Embeddings

NeurIPS 2023poster

Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels. However, GSSL methods are…

2022

Pre-training Molecular Graph Representation with 3D Geometry

ICLR 2022poster

Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically modeled by their 2D topological structures, but it has been recently discovered that 3D geometric information plays a more vital role in predicting molecular functio…

2021

Unsupervised Point Cloud Pre-Training via Occlusion Completion

ICCV 2021poster

We describe a simple pre-training approach for point clouds. It works in three steps: 1. Mask all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the occluded points; 3. Use the encoder weights as initialisation for downstream point cloud tasks. We find that even w…

Cited by 300PDFcodeScholar