{
  "id": "2111.12731",
  "version": "v2",
  "published": "2021-11-24T19:00:07.000Z",
  "updated": "2022-02-20T17:28:21.000Z",
  "title": "Human Pose Manipulation and Novel View Synthesis using Differentiable Rendering",
  "authors": [
    "Guillaume Rochette",
    "Chris Russell",
    "Richard Bowden"
  ],
  "comment": "Accepted at Face and Gesture 2021, 8 pages, 7 figures",
  "categories": [
    "cs.CV"
  ],
  "abstract": "We present a new approach for synthesizing novel views of people in new poses. Our novel differentiable renderer enables the synthesis of highly realistic images from any viewpoint. Rather than operating over mesh-based structures, our renderer makes use of diffuse Gaussian primitives that directly represent the underlying skeletal structure of a human. Rendering these primitives gives results in a high-dimensional latent image, which is then transformed into an RGB image by a decoder network. The formulation gives rise to a fully differentiable framework that can be trained end-to-end. We demonstrate the effectiveness of our approach to image reconstruction on both the Human3.6M and Panoptic Studio datasets. We show how our approach can be used for motion transfer between individuals; novel view synthesis of individuals captured from just a single camera; to synthesize individuals from any virtual viewpoint; and to re-render people in novel poses. Code and video results are available at https://github.com/GuillaumeRochette/HumanViewSynthesis.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2022-02-20T17:28:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "novel view synthesis",
      "human pose manipulation",
      "differentiable rendering",
      "novel differentiable renderer enables",
      "individuals"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}