title: BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike
    Animated Motion
summary: We show, for the first time, that neural networks trained only on synthetic
  data achieve state-of-the-art accuracy on the problem of 3D human pose and
  shape (HPS) estimation from real images. Previous synthetic datasets have been
  small, unrealistic, or lacked realistic clothing. Achieving sufficient realism
  is non-trivial and we show how to do this for full bodies in motion.
  Specifically, our BEDLAM dataset contains monocular RGB videos with
  ground-truth 3D bodies in SMPL-X format. It includes a diversity of body
  shapes, motions, skin tones, hair, and clothing. The clothing is realistically
  simulated on the moving bodies using commercial clothing physics simulation. We
  render varying numbers of people in realistic scenes with varied lighting and
  camera motions. We then train various HPS regressors using BEDLAM and achieve
  state-of-the-art accuracy on real-image benchmarks despite training with
  synthetic data. We use BEDLAM to gain insights into what model design choices
  are important for accuracy. With good synthetic training data, we find that a
  basic method like HMR approaches the accuracy of the current SOTA method
  (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth
  bodies, 3D clothing, support code, and more are available for research
  purposes. Additionally, we provide detailed information about our synthetic
  data generation pipeline, enabling others to generate their own datasets. See
  the project page: https://bedlam.is.tue.mpg.de/.
paper_authors._id: 649e5ff927145c4463174eb5
paper_authors.name: Michael J. Black
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-03T07:27:15.294Z
paper_authors._id: 649fbababa7a05e3626546c8
paper_authors.avatarUrl: /avatars/a9aa4b4914649527094fc1b12779e014.svg
paper_authors.fullname: Michael J. Black
paper_authors.type: user
paper_authors.user: MichaelJBlack
paper_authors._id: 649e5ff927145c4463174eb6
paper_authors.name: Priyanka Patel
paper_authors._id: 649e5ff927145c4463174eb7
paper_authors.name: Joachim Tesch
paper_authors._id: 649e5ff927145c4463174eb8
paper_authors.name: Jinlong Yang
paper_title: BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike
    Animated Motion
paper_summary: We show, for the first time, that neural networks trained only on synthetic
  data achieve state-of-the-art accuracy on the problem of 3D human pose and
  shape (HPS) estimation from real images. Previous synthetic datasets have been
  small, unrealistic, or lacked realistic clothing. Achieving sufficient realism
  is non-trivial and we show how to do this for full bodies in motion.
  Specifically, our BEDLAM dataset contains monocular RGB videos with
  ground-truth 3D bodies in SMPL-X format. It includes a diversity of body
  shapes, motions, skin tones, hair, and clothing. The clothing is realistically
  simulated on the moving bodies using commercial clothing physics simulation. We
  render varying numbers of people in realistic scenes with varied lighting and
  camera motions. We then train various HPS regressors using BEDLAM and achieve
  state-of-the-art accuracy on real-image benchmarks despite training with
  synthetic data. We use BEDLAM to gain insights into what model design choices
  are important for accuracy. With good synthetic training data, we find that a
  basic method like HMR approaches the accuracy of the current SOTA method
  (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth
  bodies, 3D clothing, support code, and more are available for research
  purposes. Additionally, we provide detailed information about our synthetic
  data generation pipeline, enabling others to generate their own datasets. See
  the project page: https://bedlam.is.tue.mpg.de/.
paper_ai_keywords: neural networks 3D human pose and shape (HPS) estimation BEDLAM dataset monocular RGB videos ground truth 3D bodies SMPL-X format body shapes motions skin tones hair clothing Clothing physics simulation HPS regressors SOTA method (CLIFF) HMR (Human Mesh Recovery)