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/
generative model Motion Matching bidirectional visual similarity generative cost function multi-stage framework exemplar motion matches motion completion key frame-guided generation infinite looping motion reassembly