title: Example-based Motion Synthesis via Generative Motion Matching
summary: We present GenMM, a generative model that "mines" as many diverse motions as
  possible from a single or few example sequences. In stark contrast to existing
  data-driven methods, which typically require long offline training time, are
  prone to visual artifacts, and tend to fail on large and complex skeletons,
  GenMM inherits the training-free nature and the superior quality of the
  well-known Motion Matching method. GenMM can synthesize a high-quality motion
  within a fraction of a second, even with highly complex and large skeletal
  structures. At the heart of our generative framework lies the generative motion
  matching module, which utilizes the bidirectional visual similarity as a
  generative cost function to motion matching, and operates in a multi-stage
  framework to progressively refine a random guess using exemplar motion matches.
  In addition to diverse motion generation, we show the versatility of our
  generative framework by extending it to a number of scenarios that are not
  possible with motion matching alone, including motion completion, key
  frame-guided generation, infinite looping, and motion reassembly. Code and data
  for this paper are at https://wyysf-98.github.io/GenMM/
paper_authors._id: 64798debb77e18dbf6409c83
paper_authors.name: Weiyu Li
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-10-23T16:07:07.491Z
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paper_authors.fullname: weiyuli
paper_authors.type: user
paper_authors.user: wyysf
paper_authors._id: 64798debb77e18dbf6409c84
paper_authors.name: Xuelin Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-05T12:11:37.775Z
paper_authors._id: 641d9eda101394233c083873
paper_authors.avatarUrl: /avatars/c3f68a020fa169c2040277e3dd3481e5.svg
paper_authors.fullname: chenxuelin
paper_authors.type: user
paper_authors.user: michishenme
paper_authors._id: 64798debb77e18dbf6409c85
paper_authors.name: Peizhuo Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-30T13:46:18.678Z
paper_authors._id: 66a8e7d4a203add9773fe968
paper_authors.avatarUrl: /avatars/0f42c55a02aef4bcca64f774fbd23c15.svg
paper_authors.fullname: Peizhuo Li
paper_authors.type: user
paper_authors.user: PeizhuoL
paper_authors._id: 64798debb77e18dbf6409c86
paper_authors.name: Olga Sorkine-Hornung
paper_authors._id: 64798debb77e18dbf6409c87
paper_authors.name: Baoquan Chen
paper_title: Example-based Motion Synthesis via Generative Motion Matching
paper_summary: We present GenMM, a generative model that "mines" as many diverse motions as
  possible from a single or few example sequences. In stark contrast to existing
  data-driven methods, which typically require long offline training time, are
  prone to visual artifacts, and tend to fail on large and complex skeletons,
  GenMM inherits the training-free nature and the superior quality of the
  well-known Motion Matching method. GenMM can synthesize a high-quality motion
  within a fraction of a second, even with highly complex and large skeletal
  structures. At the heart of our generative framework lies the generative motion
  matching module, which utilizes the bidirectional visual similarity as a
  generative cost function to motion matching, and operates in a multi-stage
  framework to progressively refine a random guess using exemplar motion matches.
  In addition to diverse motion generation, we show the versatility of our
  generative framework by extending it to a number of scenarios that are not
  possible with motion matching alone, including motion completion, key
  frame-guided generation, infinite looping, and motion reassembly. Code and data
  for this paper are at https://wyysf-98.github.io/GenMM/
paper_ai_summary: GenMM efficiently generates diverse, high-quality motions from limited examples using generative motion matching with bidirectional visual similarity, demonstrating versatility in various scenarios beyond standard motion matching.
paper_ai_keywords: generative model Motion Matching bidirectional visual similarity generative cost function motion matching module multi-stage framework motion completion key frame-guided generation infinite looping motion reassembly