Motion Synthesis
87 papers with code • 9 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in Motion Synthesis
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Datasets
HumanML3D
KIT Motion-Language
HumanAct12
AIST++
InterHuman
BRACE
LaFAN1
CHAIRS dataset
Latest papers
in2IN: Leveraging individual Information to Generate Human INteractions
For this, we introduce in2IN, a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction.
ParCo: Part-Coordinating Text-to-Motion Synthesis
qrzou/parco
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However, these methods encounter challenges such as the lack of coordination between different part motions and difficulties for networks to understand part concepts.
Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance
Despite significant advancements in text-to-motion synthesis, generating language-guided human motion within 3D environments poses substantial challenges.
Driving Animatronic Robot Facial Expression From Speech
The proposed approach is capable of generating highly realistic, real-time facial expressions from speech on an animatronic face, significantly advancing robots' ability to replicate nuanced human expressions for natural interaction.
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
li-ronghui/LODGE
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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.
Seamless Human Motion Composition with Blended Positional Encodings
Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics.
Self-Correcting Self-Consuming Loops for Generative Model Training
nate-gillman/self-correcting-self-consuming
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As synthetic data becomes higher quality and proliferates on the internet, machine learning models are increasingly trained on a mix of human- and machine-generated data.
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
With the emergence of generative AI models such as large language models (LLMs) and text-driven motion synthesis models, language has become a promising source data modality as well as shown in proof of concepts such as IMUGPT.
GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation
xuehao-gao/guess
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The whole text-driven human motion synthesis problem is then divided into multiple abstraction levels and solved with a multi-stage generation framework with a cascaded latent diffusion model: an initial generator first generates the coarsest human motion guess from a given text description; then, a series of successive generators gradually enrich the motion details based on the textual description and the previous synthesized results.
FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing
mingyuan-zhang/FineMoGen
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NeurIPS 2023
Notably, FineMoGen further enables zero-shot motion editing capabilities with the aid of modern large language models (LLM), which faithfully manipulates motion sequences with fine-grained instructions.
