Hugging Face Trending Papers
ECHO-G: Embodied Co-speech Humanoid mOtion Generation
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arXiv AI
3d ago
ECHO-G: Embodied Co-speech Humanoid mOtion Generation
ECHO-G is a framework for generating full‑body co‑speech motion for humanoid robots, jointly conditioned on speech audio and timed transcripts. Its Speech‑Grounded Diffusion Transformer (SGDiT) fuses frame‑aligned acoustic features with token‑level linguistic context, preserving distinct granularities while modeling one‑to‑many utterance‑motion relationships directly in robot space. The authors introduce a BEAT2‑derived audio‑text‑robot dataset, a benchmark for co‑speech characteristics, robot‑motion quality, and runtime efficiency, and demonstrate that direct robot‑space generation outperforms human‑motion generation and retargeting pipelines, with joint audio‑text conditioning yielding superior results in both quantitative evaluation and a video‑rating study. "whyItMatters":"The study provides a new dataset, benchmark, and a demonstrably effective method for generating realistic co‑speech motion directly in robot space, advancing practical humanoid robot interaction."
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
Sep 1
RoboGesture: Real-Time Semantic-aligned Co-Speech Gestures Generation for Humanoid Interaction
arXiv:2608.28693v1 Announce Type: cross Abstract: Enabling humanoid robots to respond to human speech with synchronized and semantically meaningful gestures is fundamental to natural human-robot inte...
arXiv AI
Jul 17
Semantic Audio-driven Understanding for Dynamic Humanoid Whole Body Control
arXiv:2607. 14182v1 Announce Type: cross Abstract: Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies.
Hugging Face Trending Papers
Sep 16
VibeAvatar: Aligning Phonetic Kinematics and Human Aesthetics for High-Fidelity Talking Avatar Synthesis
Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusion-based methods, existing approaches still strugg...
arXiv AI
3d ago
GestAdapt: Workspace-Conditioned Co-Speech Gesture Generation for Humanoid Robots
GestAdapt is a framework that generates co‑speech gestures conditioned on a specified wrist workspace, allowing humanoid robots to adapt their motions to environmental constraints such as walls. The system learns from six co‑speech corpora using a shared motion representation and can be retargeted to different robot embodiments. Experiments show that GestAdapt’s motions stay close to real‑motion distributions, achieve higher quality scores than a no‑workspace baseline, and outperform other methods in real‑robot evaluations on the Reachy2 humanoid.