title: Make-An-Animation: Large-Scale Text-conditional 3D Human Motion
    Generation
summary: Text-guided human motion generation has drawn significant interest because of
  its impactful applications spanning animation and robotics. Recently,
  application of diffusion models for motion generation has enabled improvements
  in the quality of generated motions. However, existing approaches are limited
  by their reliance on relatively small-scale motion capture data, leading to
  poor performance on more diverse, in-the-wild prompts. In this paper, we
  introduce Make-An-Animation, a text-conditioned human motion generation model
  which learns more diverse poses and prompts from large-scale image-text
  datasets, enabling significant improvement in performance over prior works.
  Make-An-Animation is trained in two stages. First, we train on a curated
  large-scale dataset of (text, static pseudo-pose) pairs extracted from
  image-text datasets. Second, we fine-tune on motion capture data, adding
  additional layers to model the temporal dimension. Unlike prior diffusion
  models for motion generation, Make-An-Animation uses a U-Net architecture
  similar to recent text-to-video generation models. Human evaluation of motion
  realism and alignment with input text shows that our model reaches
  state-of-the-art performance on text-to-motion generation.
paper_authors._id: 646421964bf912292228e118
paper_authors.name: Samaneh Azadi
paper_authors._id: 646421964bf912292228e119
paper_authors.name: Akbar Shah
paper_authors._id: 646421964bf912292228e11a
paper_authors.name: Thomas Hayes
paper_authors._id: 646421964bf912292228e11b
paper_authors.name: Devi Parikh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T09:34:36.752Z
paper_authors._id: 6340753995c20b9447379c67
paper_authors.avatarUrl: /avatars/f793c00a98335cd8f7efce41d403da16.svg
paper_authors.fullname: Devi Parikh
paper_authors.type: user
paper_authors.user: deviparikh-genai
paper_authors._id: 646421964bf912292228e11c
paper_authors.name: Sonal Gupta
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T09:35:08.245Z
paper_authors._id: 634ecb814677a5891c0c7026
paper_authors.avatarUrl: /avatars/a20cf325e4fe0a227ff80694ffea4a3d.svg
paper_authors.fullname: Sonal Gupta
paper_authors.type: user
paper_authors.user: thenextgenai
paper_title: Make-An-Animation: Large-Scale Text-conditional 3D Human Motion
    Generation
paper_summary: Text-guided human motion generation has drawn significant interest because of
  its impactful applications spanning animation and robotics. Recently,
  application of diffusion models for motion generation has enabled improvements
  in the quality of generated motions. However, existing approaches are limited
  by their reliance on relatively small-scale motion capture data, leading to
  poor performance on more diverse, in-the-wild prompts. In this paper, we
  introduce Make-An-Animation, a text-conditioned human motion generation model
  which learns more diverse poses and prompts from large-scale image-text
  datasets, enabling significant improvement in performance over prior works.
  Make-An-Animation is trained in two stages. First, we train on a curated
  large-scale dataset of (text, static pseudo-pose) pairs extracted from
  image-text datasets. Second, we fine-tune on motion capture data, adding
  additional layers to model the temporal dimension. Unlike prior diffusion
  models for motion generation, Make-An-Animation uses a U-Net architecture
  similar to recent text-to-video generation models. Human evaluation of motion
  realism and alignment with input text shows that our model reaches
  state-of-the-art performance on text-to-motion generation.
paper_ai_summary: A text-conditioned human motion generation model, Make-An-Animation, achieves state-of-the-art performance by learning from large-scale image-text datasets and using a U-Net architecture for both spatial and temporal modeling.
paper_ai_keywords: diffusion models Make-An-Animation text-conditioned human motion generation U-Net architecture large-scale image-text datasets temporal dimension text-to-motion generation