PAPR: Proximity Attention Point Rendering

🌟 NeurIPS 2023 Spotlight 🌟

APEX Lab, Simon Fraser University
* Denotes Equal Contribution.


Given a set of images from different views and their corresponding camera poses, PAPR learns a point-based surface representation of the scene and a rendering pipeline from scratch. Additionally, PAPR enables practical applications such as geometry editing, object manipulation, texture transfer, and exposure control.

Qualitative Results

We show the RGB rendering of the scene in the first row and the corresponding learnt point cloud in the second row.

Geometry Editing

We can edit the geometry of the scene by simply manipulating the point cloud of the scene without any additional supervision. Here we demonstrate rigid bending motions applied to the ficus branch and the Lego bulldozer's arm in the first column, roation of the statue's head and the ship in the second column, and non-volume preserving stretching transformations applied to the tip of the microphone and the back of the chair in the third column.

Object Manipulation

We can edit the scene by adding, removing or duplicating points in the point cloud. Here we demonstrate the addition of an extra hotdog to the plate (left), and the removal of certain material balls while duplicating others (right).

Texture Transfer

We can transfer the texture from one part of the scene to another by transferring the associated feature vectors of the corresponding points. Here we transfer the texture of the mustard to the ketchup by transferring the features of the points that correspond to the mustard (highlighted in yellow) to a subset of points that correspond to the ketchup (highlighted in red).

Exposure Control

We introduce an additional latent code input into our model and train it using a technique called conditional Implicit Maximum Likelihood Estimation (cIMLE). During test time, we can manipulate the exposure of the rendered image by changing the latent code input.


  author    = {Zhang*, Yanshu and Peng*, Shichong and Moazeni, Alireza and Li, Ke},
  title     = {PAPR: Proximity Attention Point Rendering},
  journal   = {Advances in Neural Information Processing Systems},
  year      = {2023},