Example-based Motion Synthesis via Generative Motion Matching
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/
64798debb77e18dbf6409c83
Weiyu Li
extracted_confirmed
2023-10-23T16:07:07.491Z
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weiyuli
user
wyysf
64798debb77e18dbf6409c84
Xuelin Chen
admin_assigned
2023-06-05T12:11:37.775Z
641d9eda101394233c083873
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chenxuelin
user
michishenme
64798debb77e18dbf6409c85
Peizhuo Li
claimed_verified
2024-07-30T13:46:18.678Z
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Peizhuo Li
user
PeizhuoL
64798debb77e18dbf6409c86
Olga Sorkine-Hornung
64798debb77e18dbf6409c87
Baoquan Chen
Example-based Motion Synthesis via Generative Motion Matching
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/
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.
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