arXiv Computer Vision
RoboGesture: Real-Time Semantic-aligned Co-Speech Gestures Generation for Humanoid Interaction
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arXiv AI
Jun 19
PhysDrift: Bridging the Embodiment Gap in Humanoid Co-Speech Motion Generation
arXiv:2606. 19935v1 Announce Type: new Abstract: Humanoid robots require co-speech motions that are not only expressive and speech-aligned, but also physically executable under embodiment constraints.
arXiv Computer Vision
Aug 27
Super Star: Towards Streaming Real-time Interactive Agents for Digital Humans
The paper introduces a real‑time framework for generating co‑speech gestures for digital humans, coupling a streaming speech response module with a causal multimodal autoregressive gesture generator that uses only current speech and motion history. It also presents an offline data synthesis pipeline for virtual companion dialogues and a self‑evolving training loop that incorporates user feedback to continually adapt the model. Experiments show the system achieves a better latency‑quality trade‑off, stronger speech‑motion synchronization, and higher user preference than existing baselines.
arXiv AI
Jun 18
Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs
arXiv:2606. 18747v1 Announce Type: cross Abstract: Expressive gestures are essential for natural and effective communication, complementing speech when verbal cues alone are insufficient (e.
arXiv Computer Vision
Sep 2
Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation
arXiv:2609.00369v1 Announce Type: new Abstract: Generating co-speech gestures that are temporally coherent, semantically aligned with speech, and grounded with surrounding objects remains challenging...
arXiv Computer Vision
Aug 27
InteractGesture: Progressive Chunk Guidance for Continuous Streaming Co-Speech Gesture Control
InteractGesture is a model‑agnostic, inference‑time method that enables fine‑grained spatial control of individual joints in continuous streaming co‑speech gesture generation. It guides diffusion sampler latent estimates through a differentiable RVQ‑VAE decoder, backpropagating spatial control gradients to adjust motion latents during sampling. To address chunk‑wise dependency issues in streaming generation, the method introduces Progressive Chunk Guidance, a chunk‑window strategy that keeps an active set of editable chunk latents with staggered delays, allowing spatial constraints to propagate gradients backward across chunk boundaries and reducing boundary inconsistencies.
arXiv AI
Jul 1
LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music
arXiv:2606. 31158v1 Announce Type: cross Abstract: The quest for intuitive and natural human-robot interaction (HRI) remains a significant challenge in robotics.