Automated Synthesis of Facial Mechanisms for Conversational Animatronic Robots

• URL: http://arxiv.org/abs/2607.11688v1
• Date: Mon, 13 Jul 2026 15:22:46 GMT
• Title: Automated Synthesis of Facial Mechanisms for Conversational Animatronic Robots
• Abstract summary: State-of-the-art animatronic faces are typically tailored systems, making large-scale personalization slow and costly.<n>We propose a parametric, linkage-driven mechanical face template whose topology and actuator layout are explicitly parameterized to support systematic scaling.<n>We develop a dual-identity conversational facial motion synthesis framework that jointly models speaking and listening behaviors from audio.
• Score: 56.50769770726642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Animatronic faces are a central component of socially interactive robots, enabling rich nonverbal communication through facial articulation. However, state-of-the-art animatronic faces are typically tailored systems: each new facial geometry requires extensive manual mechanical redesign, making large-scale personalization prohibitively slow and costly. In this work, we pursue automated and scalable mechanical face synthesis, aiming to rapidly generate a physically realizable facial mechanism for a wide range of facial geometries. We introduce a parametric, linkage-driven mechanical face template whose topology and actuator layout are explicitly parameterized to support systematic scaling and retargeting across diverse facial morphologies. Building on this template, we propose a hierarchical automatic design algorithm that takes a single 2D portrait as input, reconstructs a target 3D face, and synthesizes a collision-free, manufacturable internal mechanism. The algorithm combines anatomy-guided feasible motion volumes, Action Unit (AU)-derived trajectory-based expressiveness objectives, and a collision-driven outer-loop refinement strategy. Beyond hardware synthesis, we argue that future mechanical faces deployed at scale must engage in bidirectional, multi-turn conversation rather than functioning solely as speaking or listening heads. To this end, we develop a dual-identity conversational facial motion synthesis framework that jointly models speaking and listening behaviors from audio, producing temporally coherent 3D facial motion suitable for physical execution. We validate our system through extensive experiments, including (i) quantitative evaluation of automatic mechanism synthesis across diverse facial geometries, (ii) comparisons against manual mechanical design, (iii) benchmarks on conversational facial motion synthesis and real-time deployment, and (iv) perceptual user studies.

Related papers

• Multiple View Neural Regression of a Facial Shape Model [5.202950948929751]
  VarIS is a custom light sphere for capturing high-resolution stereo geometry and reflectance maps.<n>A deep learning framework is proposed to directly predict re-topologized facial meshes from synthetic multiview images.<n>The system produces standardized meshes ready for rigging and animation with minimal human supervision.
  arXiv  Detail & Related papers  (2026-08-23T23:31:44Z)
• MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction [54.36564144414704]
  MeshMimic is an innovative framework that bridges 3D scene reconstruction and embodied intelligence to enable humanoid robots to learn coupled "motion-terrain" interactions directly from video.<n>By leveraging state-of-the-art 3D vision models, our framework precisely segments and reconstructs both human trajectories and the underlying 3D geometry of terrains and objects.
  arXiv  Detail & Related papers  (2026-02-17T17:09:45Z)
• Real-Time Synchronized Interaction Framework for Emotion-Aware Humanoid Robots [0.5156484100374058]
  We present a real-time framework for NAO robots that synchronizes speech prosody with full-body gestures.<n>By enabling seamless sensorimotor coordination, this framework advances the deployment of context-aware social robots.
  arXiv  Detail & Related papers  (2026-01-24T03:53:09Z)
• Decoupled Generative Modeling for Human-Object Interaction Synthesis [35.78156236836254]
  Existing approaches often require manually specified intermediate waypoints and place all optimization objectives on a single network.<n>We propose Decoupled Generative Modeling for Human-Object Interaction Synthesis (DecHOI)<n>A trajectory generator first produces human and object trajectories without prescribed waypoints, and an action generator conditions on these paths to synthesize detailed motions.
  arXiv  Detail & Related papers  (2025-12-22T05:33:59Z)
• Semantic Co-Speech Gesture Synthesis and Real-Time Control for Humanoid Robots [5.531678625546847]
  We present an innovative end-to-end framework for synthesizing semantically meaningful co-speech gestures and deploying them in real-time on a humanoid robot.<n>Our core innovation lies in the meticulous integration of a semantics-aware gesture synthesis module.<n>We demonstrate that our combined system produces semantically appropriate and rhythmically coherent gestures.
  arXiv  Detail & Related papers  (2025-12-19T02:55:10Z)
• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• Is Single-View Mesh Reconstruction Ready for Robotics? [78.14584238127338]
  We evaluate single-view mesh reconstruction models for their potential in enabling instant digital twin creation for real-time planning and dynamics prediction using physics simulators for robotic manipulation.<n>Our findings highlight critical gaps between computer vision advances and robotics needs, guiding future research at this intersection.
  arXiv  Detail & Related papers  (2025-05-23T14:35:56Z)
• Jointly Understand Your Command and Intention:Reciprocal Co-Evolution between Scene-Aware 3D Human Motion Synthesis and Analysis [80.50342609047091]
  Scene-aware text-to-human synthesis generates diverse indoor motion samples from the same textual description.<n>We propose a cascaded generation strategy that factorizes text-driven scene-specific human motion generation into three stages.<n>We jointly improve realistic human motion synthesis and robust human motion analysis in 3D scenes.
  arXiv  Detail & Related papers  (2025-03-01T06:56:58Z)
• Driving Animatronic Robot Facial Expression From Speech [7.8799497614708605]
  This paper introduces a novel, skinning-centric approach to drive animatronic robot facial expressions from speech input. The proposed approach employs linear skinning (LBS) as a unifying representation, guiding innovations in both embodiment design and motion synthesis. This approach demonstrates the capability to produce highly realistic facial expressions on an animatronic face in real-time at over 4000 fps on a single Nvidia GTX 4090.
  arXiv  Detail & Related papers  (2024-03-19T12:11:57Z)
• Pose-Controllable 3D Facial Animation Synthesis using Hierarchical Audio-Vertex Attention [52.63080543011595]
  A novel pose-controllable 3D facial animation synthesis method is proposed by utilizing hierarchical audio-vertex attention. The proposed method can produce more realistic facial expressions and head posture movements.
  arXiv  Detail & Related papers  (2023-02-24T09:36:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.