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Hanyang Mao

dblp:228/1368 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Computer animation and physical simulation · 70% Audio and music processing · 30%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer animation and physical simulation › motion synthesis
choreography generation
0.312018
Dance with Melody: An LSTM-autoencoder Approach to Music-oriented Dance Synthesis · ACM Multimedia 2018
Computer animation and physical simulation › motion synthesis › human motion synthesis
dance generation
0.312018
AniDance: Real-Time Dance Motion Synthesize to the Song · ACM Multimedia 2018
Computer animation and physical simulation
music-driven dance generation
0.312018
AniDance: Real-Time Dance Motion Synthesize to the Song · ACM Multimedia 2018
Audio and music processing
music analysis
0.112018
AniDance: Real-Time Dance Motion Synthesize to the Song · ACM Multimedia 2018

Methods — techniques the papers use, named apart from their topics

LSTM autoencoder · 0.7temporal indexing · 0.3masking · 0.3
YearPublicationVenuePosition
2018 Dance with Melody: An LSTM-autoencoder Approach to Music-oriented Dance Synthesis
abstract
Dance is greatly influenced by music. Studies on how to synthesize music-oriented dance choreography can promote research in many fields, such as dance teaching and human behavior research. Although considerable effort has been directed toward investigating the relationship between music and dance, the synthesis of appropriate dance choreography based on music remains an open problem. There are two main challenges: 1) how to choose appropriate dance figures, i.e., groups of steps that are named and specified in technical dance manuals, in accordance with music and 2) how to artistically enhance choreography in accordance with music. To solve these problems, in this paper, we propose a music-oriented dance choreography synthesis method using a long short-term memory (LSTM)-autoencoder model to extract a mapping between acoustic and motion features. Moreover, we improve our model with temporal indexes and a masking method to achieve better performance. Because of the lack of data available for model training, we constructed a music-dance dataset containing choreographies for four types of dance, totaling 907,200 frames of 3D dance motions and accompanying music, and extracted multidimensional features for model training. We employed this dataset to train and optimize the proposed models and conducted several qualitative and quantitative experiments to select the best-fitted model. Finally, our model proved to be effective and efficient in synthesizing valid choreographies that are also capable of musical expression.
Taoran Tang, Jia Jia 0001, Hanyang Mao
ACM Multimedia3
2018 AniDance: Real-Time Dance Motion Synthesize to the Song
abstract
In this paper, we present a demo named AniDance that can synthesize dance motions with melody in real-time. When users sing a song or play one in their phone to AniDance, their melody will drive the 3D-space character to dance to create a lively dance animation. In practice, we conduct a music oriented 3D-space dance motion dataset by capturing real dance performances, using LSTM-autoencoder to identify the relation between music and dance. Based on these technologies, users can create valid choreographies that capable of musical expression, witch can promote their learning ability and interest in dance and music.
Taoran Tang, Hanyang Mao, Jia Jia 0001
ACM Multimedia2