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Found 9 papers, 3 papers with code
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
In contrast, the second-stage is the local diffusion, which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules.
Ranked #1 on
Motion Synthesis
on FineDance
MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models
Gesture synthesis is a vital realm of human-computer interaction, with wide-ranging applications across various fields like film, robotics, and virtual reality.
Harmonious Group Choreography with Trajectory-Controllable Diffusion
Specifically, to tackle dancer collisions, we introduce a Dance-Beat Navigator capable of generating trajectories for multiple dancers based on the music, complemented by a Distance-Consistency loss to maintain appropriate spacing among trajectories within a reasonable threshold.
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion
Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images.
Exploring Multi-Modal Control in Music-Driven Dance Generation
Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process.
Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven Body Controllable Attribute
Generating 3D human models directly from text helps reduce the cost and time of character modeling.
Chain of Generation: Multi-Modal Gesture Synthesis via Cascaded Conditional Control
We introduce a novel method that separates priors from speech and employs multimodal priors as constraints for generating gestures.
FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation
To address these problems, we propose FineDance, which contains 14. 6 hours of music-dance paired data, with fine-grained hand motions, fine-grained genres (22 dance genres), and accurate posture.
Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition
The first part is a Multi-View Spatial-Temporal Feature Extractor Network (MSTN), which can directly extract the spatial-temporal features of RGB and skeleton data; the second is a sign language encoder network based on Transformer, which can learn long-term dependencies; the third is a Connectionist Temporal Classification (CTC) decoder network, which is used to predict the whole meaning of the continuous sign language.
