@article{peng2026rigpapr,
  title={RigPAPR: Rig-Based Animation of Static Neural Point Clouds from a Fixed-Viewpoint Video},
  author={Shichong Peng and Yanshu Zhang and Ke Li},
  journal={arXiv preprint arXiv:2606.06685},
  year={2026}
}

TL;DRRig and animate a static point cloud from a single fixed-viewpoint video
RigPAPR auto-rigs a static neural point cloud and drives it with direct linear blend skinning from a single fixed-viewpoint driving video, recovering a re-posable 3D asset. Because PAPR carries no per-primitive shape, the surface re-forms naturally under articulation, avoiding the joint-boundary gaps and spikes seen in Gaussian-splatting and mesh-proxy baselines.

@inproceedings{zhang2026pointgt,
  title={PointGT: Simultaneous Geometry and Texture Editing for Point-Based Representations},
  author={Yanshu Zhang and George Shramko and Pratul P. Srinivasan and Ke Li},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}

TL;DRAttention-based point renderer + deformation aware UV mapping
PointGT combines an attention-based point representation with a learned UV mapping so that object geometry and appearance can be edited simultaneously, supporting high-resolution texture edits that persist under non-rigid geometry deformations.

@inproceedings{zhang2026pcore,
  title={P-CORE: Self-Supervised Surface Consistency for Point-Based Neural Editing},
  author={Yanshu Zhang and Shichong Peng and Mehran Aghabozorgi and Alireza Moazeni and Ke Li},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}

TL;DRSupervise surface prediction of deformed points by the deformed surface
P-CORE lets attention-based point representations undergo large non-rigid deformations without holes or tears by enforcing that the predicted surface stays consistent before and after random deformations, enabling zero-shot editing with substantially fewer artifacts.

@inproceedings{moazeni2026misattribution,
  title={Tackling Misattribution in 3D Intrinsic Decomposition via Proximity Attention Point Rendering},
  author={Alireza Moazeni and Shichong Peng and Yanshu Zhang and Chirag Vashist and Ke Li},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}

TL;DRResolving misattribution in point-based 3D intrinsic decomposition
Intrinsic PAPR identifies and fixes the misattribution issue in point-based inverse rendering, where individual primitives learn incorrect appearance despite producing correct aggregated renders. Using proximity attention point rendering for direct per-point supervision, it enables accurate, view-consistent albedo and shading editing.

@inproceedings{zhang2026paprupclose,
  title={PAPR Up-close: Close-up Neural Point Rendering without Holes},
  author={Yanshu Zhang and Chirag Vashist and Shichong Peng and Ke Li},
  booktitle={International Conference on 3D Vision},
  year={2026}
}

TL;DRHole-free close-up neural point rendering
We extend PAPR for robust close-up neural point rendering and significantly reduce holes and artifacts while preserving fine details.

@inproceedings{aghabozorgi2026wimle,
  title={{WIMLE}: Uncertainty-Aware World Models with {IMLE} for Sample-Efficient Continuous Control},
  author={Mehran Aghabozorgi and Alireza Moazeni and Yanshu Zhang and Ke Li},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026}
}

TL;DRMulti-modal world models with uncertainty-aware policy learning for model-based RL
WIMLE learns stochastic, multi-modal world models using IMLE and weights synthetic transitions by predictive confidence, achieving state-of-the-art sample efficiency across 40 continuous-control tasks in DeepMind Control, HumanoidBench, and MyoSuite.

@inproceedings{peng2024papr,
  title={PAPR in Motion: Seamless Point-level 3D Scene Interpolation},
  author={Shichong Peng and Yanshu Zhang and Ke Li},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

TL;DRSeamless point-level 4D motion interpolation
We introduce the novel problem of point-level 3D scene interpolation. Given observations of a scene at two distinct states from multiple views, the goal is to synthesize a smooth point-level interpolation between them, without any intermediate supervision. Our method, PAPR in Motion, builds upon Proximity Attention Point Rendering (PAPR) technique, and generates seamless interpolations of both the scene geometry and appearance.

@inproceedings{zhang2023papr,
  title={PAPR: Proximity Attention Point Rendering},
  author={Yanshu Zhang and Shichong Peng and Alireza Moazeni and Ke Li},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}

TL;DRReconstruct and render point clouds using attention
PAPR is a point-based surface representation that uses proximity attention to interpolate between nearby points to rays for rendering high-quality images, enabling non-volume-preserving geometry deformation by directly adjusting point positions, and, unlike 3D Gaussian Splatting, it avoids creating holes while preserving texture details after deformation.