Xingang Pan

@XingangP

Assistant Professor at Nanyang Technological University @NTUsg @MMLabNTU - Generative AI, Computer Vision

Singapore

xingangpan.github.io

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Xingang Pan
@XingangP
Jul 17
We just released a high-quality, penetration-free cloth simulation dataset used in ClothTransformer. 👕 🤗huggingface.co/datasets/YuCra… 493K frames of cloth simulation (2,056 trajectories, ~33GB) across collision, animated human, and robotic manipulation scenarios.
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Yu Zhang
@yucrazing
Jul 17
Our ClothTransformer dataset has released now! 🤗 huggingface.co/datasets/YuCra… 493K frames penetration-free cloth simulation (2,056 trajectories, ~33GB) across 3 scenarios: 🪂 Free-fall collision onto diverse objects 👕 Garments on animated human bodies 🤖 Robotic cloth manipulation
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Xingang Pan
@XingangP
Jun 12
Natural images often already implicitly contain depth information — hidden in bokeh effects. Can we leverage the rich depth cue widely exist in natural images for depth estimation? We explore this in our recent project BokehDepth (ICML 2026). - Stage 1: A generative model
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Xingang Pan
@XingangP
Jun 9
Transformers have succeeded in modeling phenomena traditionally associated with computer graphics, such as 3D visual effects (e.g., RayZer) and rendering processes (e.g., RenderFormer). A natural question is whether they can also tackle the challenging task of cloth simulation.
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Yu Zhang
@yucrazing
Jun 9
02:22
Sharing our recent work — ClothTransformer 🧵 We propose a unified Transformer-based neural cloth simulator that solves the "one model per scenario" problem. One single model handles diverse cloth simulation scenarios — useful for digital humans, embodied AI, games & VFX.
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Xingang Pan
@XingangP
Jun 2
I'll give a talk about Video World Models🌎 at #CVPR2026 🗓️Thu, Jun 3, 11:05 AM, Room 705/707: 1st Workshop on Video World Models videoworldmodel-workshop.github.io Unfortunately, due to a visa delay (still no visa after 50 days 😅), I'll be presenting virtually this year. Nevertheless,
Xingang Pan
@XingangP
Jan 12
Introducing StoryMem — a memory-augmented framework for multi-shot long video storytelling. StoryMem carefully injects compact memory into the generation process with minimal overhead, enabling: • Cross-shot consistency • Smooth transitions • Narrative coherence across
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