OMG: Open-vocabulary Motion Generation Framework
OMG introduces a novel framework for compelling motion generation from zero-shot open-vocabulary text prompts.
Bidirectional Autoregressive Diffusion for Generating Coherent and Diverse Text-Guided Human Motions
The proposed Bidirectional Autoregressive Diffusion (BAD) framework unifies the strengths of autoregressive and mask-based generative models to effectively capture both sequential and bidirectional relationships in text-guided human motion generation.
Generating Expressive Whole-Body Motions from Text Descriptions Using Partially Annotated Data
A two-stage method that learns expressive text-to-motion generation from partially annotated data, utilizing VQ-VAE experts for high-quality motion representation and a multi-indexing GPT model for coordinating body, hand, and facial motions.
Efficient Spatial-Temporal Modeling for Text-Driven Human Motion Generation
A novel spatial-temporal modeling framework for generating human motions from textual prompts, which quantizes each joint into a 2D token map and leverages 2D operations to effectively capture the spatial-temporal relationships.
MotionRL: Using Multi-Reward Reinforcement Learning to Improve Text-to-Motion Generation by Aligning with Human Preferences
MotionRL is a novel approach that leverages reinforcement learning to fine-tune text-to-motion generation models, aligning them with human preferences and improving the quality of generated motions beyond traditional metrics.
MotionRL: 다중 보상 강화 학습을 사용하여 텍스트-모션 생성을 인간 선호도에 맞춤
인간의 움직임에 대한 인식을 기존의 수치적 지표보다 우선시하여 텍스트에서 모션을 생성하는 모델 MotionRL을 소개합니다. MotionRL은 강화 학습을 사용하여 인간의 선호도를 학습하고, 텍스트 충실도, 모션 품질 및 인간 선호도 간의 균형을 맞춰 최적의 모션을 생성합니다.
ReinDiffuse: Enhancing Motion Diffusion Models with Reinforcement Learning for Physically Plausible Human Motion Generation from Text
ReinDiffuse enhances the physical plausibility of human motion generated from text descriptions by integrating reinforcement learning with motion diffusion models, eliminating the need for computationally expensive physics simulations.
강화 확산 모델을 사용한 사실적인 움직임 생성: ReinDiffuse
텍스트에서 사실적인 인간 동작을 생성하기 위해 강화 학습과 모션 확산 모델을 결합한 새로운 접근 방식인 ReinDiffuse를 소개합니다.
Enhancing Motion Variation in Text-to-Motion Models Using Pose and Video Editing
This research introduces a novel method to enhance the variation and realism of motions generated by text-to-motion models by incorporating pose and video editing techniques, addressing the limitations posed by data scarcity in current text-motion datasets.
LEAD: Enhancing Human Motion Diffusion with Latent Realignment for Improved Realism and Textual Inversion
LEAD, a novel text-to-motion generation model, leverages latent diffusion and a realignment mechanism to create semantically structured motion latents, improving realism, expressiveness, and enabling textual motion inversion for personalized motion synthesis.
